STMicroelectronics’ ASM330LHHG1 is an automotive-qualified IMU rated for operation from -40°C to 125°C, combining a 3-axis accelerometer, a 3-axis gyroscope, temperature compensation and 6-channel synchronized output to support dead reckoning, GNSS fusion and motion-data correlation. The device offers accelerometer full-scale measurement up to ±16g, gyroscope ranges from ±125dps to ±4000dps, dual high-performance and low-power operating modes, and interfaces including I²C, MIPI I3C® and SPI, with a 3KB FIFO to reduce host processor load and power use. AEC-Q100 qualified and available in a 2.5 mm x 3.0 mm LGA-14L package, the IMU is intended for cars, trucks and industrial or agricultural vehicles in applications such as navigation, telematics, eTolling, V2X, anti-theft, crash reconstruction and vibration monitoring.

The post Automotive IMU combines synchronized 6-axis motion sensing appeared first on Sensor Tips.

TDK Corporation has added the HAL 3025 to its Micronas fast Hall sensor family for x-by-wire motor control applications. The single-die sensor measures 360° rotational angle, supports speeds up to 60,000 rpm and is designed as SEooC ASIL D ready, with magnetic stray-field compensation to ISO 11452-8, integrated diagnostics and differential or single-ended sine/cosine analog outputs. The device operates from -40°C to 170°C junction temperature in an SOIC8 package and includes programmable gain, offset, 0-angle and orthogonality settings through the output pin for end-of-line calibration.

The post Hall sensor measures 360° angle to 60,000 rpm appeared first on Sensor Tips.

This article outlines how EV manufacturers integrate temperature monitoring, current sensing, and fault detection into connector and contactor systems to prevent overheating and enable safe high-power charging and power distribution. It also explains how proximity and pilot signaling in charging interfaces provide coordinated, multi-layer protection across the full charge event.

Temperature sensing in connectors and charging interfaces

As shown in Figure 1, high-voltage (HV) connectors integrate temperature sensors at or near contact interfaces and cable terminations, where I²R losses and contact degradation create localized hot spots.

Negative temperature coefficient (NTC) thermistors packaged in high-dielectric ceramic sleeves are placed close to HV contacts. They withstand several kilovolts of isolation and operate continuously up to ~150°C, with short-term capability to 180°C–200°C.

In vehicle HV connectors, these sensors monitor battery-to-inverter and battery-to-contactor interfaces, where thermal feedback triggers current derating or contactor opening when temperature rises rapidly or exceeds defined thresholds.

In DC fast charging interfaces, similar sensing integrates with control protocols. IEC 61851 implementations define over-temperature thresholds: at ≥120°C, the system must shut down immediately, while sustained operation above ~90°C forces derating or shutdown.

Some connector designs monitor resistance or embedded sensing elements in the plug to estimate connector-head temperature in real time, allowing the electric vehicle supply equipment (EVSE) or onboard charger to reduce current progressively rather than waiting for a hard fault.

In high-EMI or very high-voltage environments such as ultra-fast charging systems and high-power busbars, fiber-optic temperature sensing provides an alternative to NTC probes. These sensors are immune to electromagnetic interference (EMI) and inherently isolated, supporting their use in validation setups and high-power charging equipment where electrical isolation is critical.

Current sensing and integrated contactor protection

High-voltage contactors previously relied on external shunt resistors or separate Hall-effect sensors to measure line current. Integrated current-sensing contactors combine the switching element and current measurement within a single device, reducing component count and tightening coupling between measured current and the actuator that interrupts it.

Some designs measure bidirectional line current across a wide DC range, up to several hundred amperes at voltages approaching 600 VDC. They may also include a programmable current-trip function that allows the contactor to open autonomously on overcurrent or short-circuit conditions without external controller intervention. Dual-coil economizer circuits with integrated coil suppression also maintain electromagnetic compatibility (EMC) within the HV compartment.

In charger power stages, current sensing extends beyond switching protection to support control-loop operation. Current transformers (CTs) provide passive, isolated measurement on AC line stages and allow cost-effective overcurrent detection at power-line frequencies.

Hall-effect sensors, particularly closed-loop compensated designs, extend measurement to DC buses and high-frequency power stages where CT bandwidth and DC response limitations constrain performance. The two technologies operate in complementary roles within the same charger architecture.

In parallel, residual current monitoring provides a standards-critical protection layer. In EV charging systems, a smooth DC residual current of approximately 6 mA can saturate conventional toroidal sensing cores used in Type A residual current device (RCD) architectures, degrading or eliminating their ability to detect superimposed AC leakage.

IEC 62955 mandates DC-capable residual current detection, driving designs toward Type B RCDs or residual current differential device (RDC-DD) implementations, as shown in Figure 2, often using fluxgate sensors.

These devices detect both AC and DC leakage with low drift over temperature and support compliance-grade detection in EVSE designs.

Fault detection and contactor weld monitoring

Beyond overcurrent protection, EV power distribution systems implement diagnostic sequences to detect mechanical faults in contactors, including welded or stuck contacts that prevent safe shutdown. These diagnostics rely on coordinated current, voltage, and position sensing interpreted by battery management system (BMS) or vehicle control unit (VCU) firmware.

One approach uses voltage-based weld detection, which applies controlled switching sequences during a diagnostic interval to infer contact state. Control logic opens one contactor, closes another, and measures the resulting voltage between nodes. If the voltage does not respond as expected, a welded contact is indicated, and further HV operation is inhibited.

Auxiliary contacts, mechanically linked to the main contacts and monitored independently, provide a complementary signal: a mismatch between the coil command state and auxiliary contact feedback indicates a weld or jam condition and triggers safe-state sequencing.

DC fast charging standards define maximum reaction times for overcurrent, short-circuit, overvoltage between DC rails, and loss of protective earth, which these diagnostic requirements must meet.

As a result, fault detection circuitry and algorithms must operate within millisecond response windows. These constraints reinforce integrated sensing architectures, where current measurement and protection logic reside close to or within the contactor rather than depending on multi-node communication latency.

Proximity, pilot, and charging interface sensing

AC and DC charging interfaces integrate dedicated sensing channels that verify connection state, cable rating, and fault conditions before enabling power and during charging.

As shown in Figure 3, the Proximity Pilot (PP) line in IEC 62196 Type 2 connectors encodes the cable conductor cross-section as a resistor value within the plug, which the EVSE measures to limit output current and prevent cable overload without requiring active communication from the vehicle.

The Control Pilot (CP) line operates in parallel, using voltage level and pulse-width modulation (PWM) duty cycle to communicate EV connection state, vehicle readiness, fault conditions, and the maximum current the vehicle may draw from the EVSE. The vehicle must follow the specified current limit, while CP state transitions are monitored continuously for faults.

Loss of proximity, CP state anomalies, overtemperature at the connector, overvoltage between DC rails, and loss of protective earth each have defined detection and reaction times that the EVSE must meet for compliance.

Together, these channels form a multi-layer interlock that maintains continuous verification of mechanical connection, cable rating, vehicle readiness, and thermal status during the charging session. A fault in any channel reduces current or terminates the session before damage to the connector, cable, or vehicle occurs.

Summary

Sensing technologies embedded in EV connectors, contactors, and charging interfaces enable coordinated protection across the HV power distribution chain. Thermal monitoring drives derating and shutdown, integrated current sensing enables fast fault response and weld detection, and DC-capable residual current monitoring addresses leakage that AC-only architectures cannot detect. Together with proximity and pilot signaling, these systems maintain continuous visibility into connection state, operating conditions, and fault behavior.

References

High-Voltage Temperature Sensors for Connectors in e-Mobility; TDK App Note, European Passive Components Institute
EV/EVSE Testing and Certification, OpenChargeAlliance
IEC 61851: Everything You Need to Know About the EV Charging Standard, eInfoChips
Proximity Pilot, IQLaaD
Relays & Contactors for EV Charging Applications, DuraKool
EV Battery Temperature Sensors, Amphenol
Reliable Temperature Monitoring During the Charging Process for xEV Batteries, TDK Electronics
Supporting EV Growth with Precision Sensors, SenScience
Thermal Management for BEVs with TDK Sensor Solutions Optimized  by Computer-Aided Modeling, TTI

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What Engineering Requirements Shape EV High-Voltage Connectors and Contactors?

The post How sensing technologies improve EV connector and contactor safety appeared first on Sensor Tips.

Teledyne DALSA has expanded configurations of the AxCIS family of fully integrated contact image sensor modules, now available in lengths up to 1,500 mm and resolutions up to 1,800 dpi for machine vision inspection in semiconductor wafer, battery and print applications. The modules combine sensors, lenses and lighting in a compact unit, delivering monochrome line rates up to 80 kHz at 14 µm pixel size or native RGB 3-line rates up to 60 kHz at 28 µm pixel size, with HDR capability, seamless full-field imaging and a telecentric lens for metrology use. AxCIS operates from a single 24 V supply and uses an SFP+ fiberoptic interface for high-throughput data transfer over long cables with EMI immunity, while its compact form factor and IP60 dust-proof optical path support installation in systems with limited vertical clearance.

The post Integrated CIS design simplifies high-speed machine vision appeared first on Sensor Tips.

Welcome to the April edition of Engineering Deep Dive — a curated selection of the most engaging technical threads from the Electro-Tech-Online community’s Electronic Projects Design/Ideas/Reviews category. Questions are selected based on view counts, reply depth, and educational value. Each entry below has been expanded with context, key concepts, and suggestions for further exploration.

Questions at a glance

Q1  Why Does My Window Comparator Output Stay ON at 0V Input Despite Correct Threshold Voltages?
Q2 Does Cable Velocity Factor Affect Pulse Propagation Delay or Only Phase Shift?
Q3  How Does Pulse Ignition and Flame Rectification Work in Gas Water Heater Ignition Systems?
Q4  How Do You Identify Whether a PCB Failure Is Caused by Design Errors or Manufacturing Defects?
Q5  Why Is Reflection Cancellation Still Considered Resonance in Time-Domain Analysis?
Q6  How Can You Measure and Isolate PCB Trace S-Parameters Without RF Connectors?
Q7  What Is the Most Convenient Way to Implement Real-Time Audio FFT Analysis?

Q1  Why does my window comparator output stay on at 0V input despite correct threshold voltages?

A window comparator circuit based on the LM339 is behaving unexpectedly — the output LED stays ON even when the input voltage is 0V, despite threshold voltages appearing correct. This thread walks through threshold calculation, LM339 open-collector output behavior, and systematic troubleshooting of component failures that cause latched outputs.

Key technical topics covered

• Window comparator design with LM339 (open-collector output stage)
• Upper and lower threshold voltage calculation
• Diagnosing latched or stuck outputs caused by faulty components
• Pull-up resistor selection and LED drive circuit

Why It Matters | Window comparators appear in battery monitors, motor-speed controllers, temperature alarms, and ADC over-range detectors. Misunderstanding open-collector outputs is one of the most common LM339 pitfalls for beginners.

Topic tags

Circuit Design | LM339 | Comparator | Troubleshooting

Supporting data: Circuit schematic image

Community thread: Thread link

Q2  Does Cable velocity factor affect pulse propagation delay or only phase shift?

When feeding a digital pulse into a coaxial or transmission-line cable, does the cable’s velocity factor (VF) introduce a propagation delay, or does it only shift the phase of a sinusoidal signal? This thread distinguishes group velocity (relevant to pulse delay) from phase velocity (relevant to sinusoidal phase shift) and explains why both are numerically identical in non-dispersive media.

Key technical topics covered

• Velocity factor and its physical origin (permittivity of the dielectric)
• Group velocity vs. phase velocity — when they differ
• Propagation delay calculation: t_d = length / (VF × c)
• Practical impact on digital timing in long cable runs

Why It Matters | Signal integrity engineers designing high-speed serial links, RF engineers building phased arrays, and hobbyists working with long cable runs all need to understand how VF affects their signals.

Topic tags

Signal Integrity | Transmission Line | RF | Digital Timing

Supporting data: Oscilloscope waveform image

Community thread: Thread link

Q3  How Does Pulse Ignition and Flame Rectification Work in Gas Water Heater Ignition Systems?

This thread goes beyond simple ignition spark generation to explore how a flame rectification sensor confirms combustion. The discussion covers the ionization current produced by a gas flame, how it is used as a half-wave rectifier in the safety circuit, and the risks of DIY modifications to gas appliance electronics.

Key technical topics covered

• High-voltage spark generation via a pulse ignition module
• Flame rectification: DC bias through an ionized gas column
• Safety interlocks and why repeated ignition failures must not be bypassed
• Troubleshooting the sensor electrode (fouling, misalignment, cracked ceramic)

Why It Matters | Gas appliance faults can be dangerous. Understanding the intended safety logic helps technicians and advanced hobbyists diagnose faults responsibly, without disabling protective interlocks.

Topic tags

Power Electronics | Safety Systems | Sensors | Gas Ignition

Supporting data: Module photograph

Community thread: Thread link

Q4  How do you identify whether a PCB failure is caused by design errors or manufacturing defects?

When a PCB batch fails, the root cause might be in the Gerber/drill files or in the fabrication process itself. This thread provides a structured methodology: cross-referencing design files with fab specifications, identifying tell-tale defect signatures (trace opens, plating voids, layer misregistration), and communicating findings to the PCB house.

Key technical topics covered

• DFM (Design for Manufacturability) review checklist before ordering
• Common fab defects: plating voids, trace opens, drill inaccuracies, solder-mask misalignment
• Layer misregistration detection via cross-section or X-ray inspection
• How to document and report defects to get boards replaced or credited

Why It Matters | PCB fabrication failures are costly in both money and schedule. A systematic approach reduces finger-pointing between design and manufacturing teams and speeds up root-cause resolution.

Topic tags

PCB Design | DFM | Manufacturing | Quality Assurance

Supporting data: PCB microscopy image

Community thread: Thread Link

Q5  Why Is Reflection Cancellation Still Considered Resonance in Time-Domain Analysis?

Resonance is traditionally taught in the frequency domain as a sharp peak at a natural frequency. This thread unpacks the conceptual bridge to the time domain: how delayed reflections, constructive/destructive interference, and oscillating energy exchange between inductance and capacitance all manifest as what we still call ‘resonance’, regardless of domain.

Key technical topics covered

• Time-domain view of resonance: energy oscillating between L and C
• Reflections on transmission lines and how they create standing waves
• Fourier duality: why time-domain oscillation maps to a frequency-domain peak
• Practical examples: stub resonance, via resonance in PCBs

Why It Matters | Signal integrity engineers and RF designers often switch between domains. Understanding why the same physical phenomenon appears as both a time-domain ringing and a frequency-domain peak prevents analysis errors.

Topic tags

Signal Integrity | RF Theory | Frequency Domain | Time Domain

Supporting data: Simulation waveform image

Community thread: Thread Link

Q6  How can you measure and isolate PCB trace S-parameters without RF connectors?

Characterizing a PCB interconnect with a VNA is straightforward when SMA connectors are available — but what if there are none? This thread covers probe-based measurement, the de-embedding process to remove fixture and pad parasitics, and the 2x-Thru method for extracting a single trace’s S-parameters from a back-to-back structure.

Key technical topics covered

• Probe landing and transition de-embedding concepts
• 2x-Thru and Short-Open-Load-Through (SOLT) calibration strategies
• Reducing fixture discontinuities with careful pad geometry
• Software tools: IDEM, OpenDEKit, or VNA manufacturer utilities

Why It Matters | As PCB speeds push into multi-GHz territory, accurate S-parameter extraction without connectors is essential for channel simulation, equalizer design, and compliance testing.

Topic tags

RF Measurement | S-Parameters | PCB | Signal Integrity

Supporting data: VNA measurement image

Community thread: Thread Link

Q7  What is the most convenient way to implement real-time audio FFT analysis?

Real-time FFT analysis of audio turns a time-domain waveform into a live frequency spectrum. This thread compares hardware (dedicated FFT modules, FPGA), microcontroller (ARM CMSIS-DSP, ESP32 FFT), and PC-based (Python, MATLAB) approaches, weighing latency, cost, and complexity.

Key technical topics covered

• FFT fundamentals: window functions, bin resolution, sample rate requirements
• Microcontroller options: ARM CMSIS-DSP library, ESP32 FFT example
• Dedicated modules: MSGEQ7 7-band analyzer IC, OpenMusicLabs FHT
• PC/software approaches: Python (numpy.fft), MATLAB, Audacity spectrum view

Why It Matters | Audio FFT is used in music visualizers, hearing aid design, acoustic testing, and voice-command pre-processing. Choosing the right platform depends on the required resolution, update rate, and available hardware.

Topic tags: Audio DSP | FFT | Embedded Systems | Signal Processing

Supporting data: N/A — community discussion

Community thread: Thread Link

Join the conversation

If any of these questions sparked an idea or you have hands-on experience with a related problem, jump into the thread — the community benefits most when engineers at all levels contribute. You can also start your own question in the Electronic Projects Design/Ideas/Reviews category on Electro-Tech-Online.

Browse all categories: electro-tech-online.com

The post Engineering deep dive-monthly forum highlights April edition appeared first on Sensor Tips.

The TMF8829 has technically been out since late 2024, but it’s now in full production and generating more customer interest than anything ams OSRAM has released before, according to OSRAM. It’s worth understanding why.

The sensor is configurable from 8 x 8 up to 48×32 pixels. That upper end is where things get interesting. At 48×32, you’re not just detecting presence, you’re starting to resolve shape, size, and volume. When combined with Edge Impulse tools, the demo ran inference on-device to classify objects in real time. Low-resolution machine vision, without a camera, without video processing overhead, and without the privacy concerns that come with optical imaging.

The field of view is 80 degrees diagonally, a significant jump from the 18-20 degrees typical of single-zone ToF sensors. Range goes up to around 10-11 meters in 8 x 8 mode. Interface is I²C or SPI, so integration fits into the workflow most embedded engineers already know.

The price point is what’s opening doors in applications that previously couldn’t justify spatial sensing. Single-unit pricing on Digi-Key runs around $14. At 3,500 pieces, it drops under $10. At meaningful production volumes, significantly lower. That math works for a lot of applications that camera-based solutions never could.

Customer projects in the pipeline span a wide range: smart appliances, MRI patient positioning, grain level measurement in industrial silos, occupancy counting in buildings and stadiums or anywhere you need to know something about spatial geometry without capturing an image. The privacy angle is real. Nobody wants cameras in a restroom, but a facilities manager still needs occupancy data. This sensor gives you that without the baggage.

The design challenge worth knowing upfront: optical design. Electronics engineers are comfortable with the PCB and firmware side. Th The optical stack, cover glass selection, and crosstalk management are where projects tend to run into trouble. ams OSRAM has an optical design guide and software tools to model crosstalk levels, but it’s worth getting the industrial designer in the room early. That’s the part nobody warns you about.

The post Sensors Converge: ams OSRAM’s low-cost computer vision without the camera appeared first on Sensor Tips.

STMicroelectronics has released the VD55G4 and VD65G4, ultralow-power global-shutter image sensors with monochrome or RGB output, detect-and-wake operation, and interfaces for low-power microcontrollers and SoCs in battery-powered or energy-harvesting devices. The sensors can reduce power consumption by up to 10x compared with conventional global-shutter sensors and shift vision systems from continuous streaming to event-driven operation, extending battery life and lowering standby power. Built for wearables, AR/VR and XR headsets, smart home products and medical devices, they also use a compact optical format and integrated image processing to simplify edge vision design and reduce system cost.

The post Ultralow-power global-shutter sensors enable always-on edge vision appeared first on Sensor Tips.

The signal processing needs of imaging radar are different from basic radar, which is only designed for detecting range and velocity. Some of the key signal processing differentiators for imaging radars include:

Multiple-input multiple-output (MIMO) and digital beamforming (DBF) antenna arrays are used to form hundreds of virtual beams, increasing angular resolution and providing the data needed to create three-dimensional (3D) point cloud images of the surroundings. That demands significant processing power.

While imaging radar is used with automotive applications, it can also be used to replace passive IR sensing and other technologies in a variety of applications, like:

• Smart home thermostats, speakers, and appliances
• Smart lighting systems and room occupancy detection
• Security devices like IP cameras and motion detection systems

Frequency selection is a first-order condition. Options include 24, 60, and 77 GHz. 77 GHz provides the best resolution. It’s currently limited to outdoor applications like advanced driver assistance systems (ADAS).

For indoor and in-cabin applications, 60 GHz has replaced 24 GHz as the most common choice. 60 GHz provides over 20x higher resolution compared with 24 GHz and can be used for sensing human breathing and even heartbeats. Some applications, like advanced gesture recognition, can benefit from the even higher resolutions enabled by 77 GHz (77 to 81 GHz band) radar.

Signal processing

SAR technology, along with advanced millimeter-wave (mmWave) radar techniques like MIMO, are used for in-cabin sensing. Originally developed for satellite and aerial imaging and mapping, SAR has been adapted to high-resolution, short-range imaging inside vehicles to detect micro-movements.

Inverse synthetic aperture radar (ISAR) can also be used to leverage the subtle micro-movements of people and objects inside a vehicle. By analyzing Doppler shifts from these movements, 60 Hz ISAR creates detailed images with resolution down to 1 cm to classify passengers, track vital signs, and detect if a child is left unattended.

DBF combined with MIMO arrays can deliver high angular resolution. Signal processing creates narrow, focused beams to increase the SNR and improve spatial separation, enabling 4D imaging (range, azimuth, elevation, Doppler).

Interference sources and solutions

The success of radar technology in an increasing variety of applications and environments can be a significant source of interference. Mutual radar interference can be particularly challenging for cars. As more vehicles are equipped with radar, signals from nearby vehicles can penetrate surrounding vehicles and interfere with the operation of their radars.

If proper design rules are not followed, intra-vehicle interference can also cause problems with multiple radars on the same vehicle interfering with each other.

Cabin environment factors like occupants, luggage, and so on, and structural factors like body vibrations caused by engine movement or driving on uneven surfaces, can introduce phase noise, often at frequencies below 100 Hz, that produces artifacts in the detected signals.

Reflections from the metallic and glass elements in the cabin, plus general clutter like beverage containers, personal electronics, and other items, can create complex multipath interference and ghosting that can make it difficult to implement the precision needed for applications like vital signs monitoring (Figure 2).

General electromagnetic interference from other vehicle electronics like switching power supplies, motor drives, the infotainment system, wireless connections, and so on can interfere with high-sensitivity radar receivers. This can be especially challenging for applications like gesture recognition.

Placement of the radar, under the seats, on the dashboard, or in the roof liner, is an important consideration for maximizing coverage and minimizing exposure to interference sources. Finally, people don’t necessarily sit still. Movement of the subjects being monitored can corrupt the measurements.

There are several electrical design techniques used for minimizing interference. For example, using different frequencies for external (70 GHz) and in-cabin (60 GHz), called frequency domain mitigation, is highly effective.

DBF is used to restrict the field of view of a receiver, effectively blocking out sources of interference. Time domain signal reconstruction can be used to repair damaged return signals, and varying the chirp timing parameters is used to reduce the probability of sustained mutual interference between radars.

Summary

Imaging radar typically operates at 60 GHz and requires specialized signal processing to produce high-resolution images, including micro-motion detection. These systems must operate reliably in multipath interference environments, while also managing interference from nearby electronics and sensors.

References

Cognitive Radar Signal Processing, NATO
FMCW Radar System Interference Mitigation Based on Time-Domain Signal Reconstruction, MDPI sensors
How Do Radars Work?, MathWorks
How to Prevent Interference When Multiple Millimeter-Wave Radars Work in Parallel, Ningbo Linpowave
Interference Characterization and Power Optimization for Automotive Radar With Directional Antenna, IEEE
Interference Mitigation For AWR/IWR Devices, Texas Instruments
Performance Evaluation of Multi-Modal Radar Signal Processing in Dense Co-Existent Environments, MDPI sensors
Signal Processing Challenges in Automotive Radar, arXiv
Understanding radar signal processing, Infineon
Volume-Based Occupancy Detection for In-Cabin Applications by Millimeter Wave Radar, MDPI remote sensing

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The post What are the signal processing and interference management considerations for imaging radar? appeared first on Sensor Tips.

It’s also used for in-cabin sensing of vehicle occupants, see: “How is radar used for automotive in-cabin sensing?”

The number of 4D radars used in advanced driver assistance systems (ADAS) varies widely. An entry-level system may have a single front-facing radar, while higher levels of autonomy require more and higher-performance radars.

High ADAS performance levels can require up to 9 or more radars, including front, rear, side, and corner locations, with varying requirements for sensing distances and resolutions (Figure 1). In addition to being dedicated to specific applications like lane changing or collision avoidance, the various radar images can be stitched together to provide a 360° awareness of the surroundings.

The basics

A 4D imaging radar uses a multiple-input multiple-output (MIMO) antenna array to create hundreds or thousands of channels, increasing angular resolution and enabling the creation of a dense “point cloud” representing the shapes and positions of multiple objects with high precision. The point cloud enables both object detection and detailed mapping of the environment, including:

• Distance measurements to support collision avoidance, blind spot detection, safe lane changes, and other functions.
• Velocity measurements track the relative speed of moving objects and anticipate possible hazards.
• Angular resolution can precisely determine the relative position of moving and stationary objects.
• Multi-object tracking for simultaneous monitoring of vehicles, pedestrians, cyclists, and other objects in the vicinity.

The addition of elevation measurements allows a 4D radar to detect the height of objects. That enables the system to distinguish between a stalled car underneath a bridge and the bridge located overhead.

Frequency-modulated continuous wave (FMCW) technology is almost universally used for 4D imaging radar, including automotive, drones, industrial process control, robotics, and other applications. FMCW continuously emits radio waves and compares the reflected waves to measure distance, velocity, and angle.

Next-gen performance

Like most areas of electronics technology, 4D radar systems are being pushed for increased performance and lower costs. For example, antenna technology has been developed that uses molded air-filled tunnels to guide waves, reducing signal loss and improving sensitivity for higher resolutions.

Systems are moving from expensive monolithic microwave integrated circuits (MMICs) to CMOS technology for better integration and cost-effectiveness.

As 4D imaging radar becomes more widely used in a greater variety of applications, integrated modules are also being increasingly used to control costs and improve performance. Cascading multiple integrated modules rather than individual transceiver chips reduces signal loss, synchronization errors, and thermal constraints. It can also reduce assembly costs, enable smaller PCB footprints, and simplify manufacturing.

Using integrated waveguide antennas or multi-chip modules can also enable higher angular resolution and longer range without high power consumption and the complex calibrations required when cascading individual chips.

Location complication

Urban planners are particularly interested in maximizing the benefits of 4D imaging radar for enhanced safety to both drivers and pedestrians. As a result, two key use cases have been developed: the Highway Pilot and Urban Pilot. Imaging radar is expected to satisfy the needs of both.

The Highway Pilot focuses on the open road and requires 4D radar that can detect rapidly moving objects over relatively long distances to support safe high-speed maneuvering. The Urban Pilot focuses on more complex environments in closer proximity, including both moving and stationary objects, and requires simultaneous detection and classification of multiple objects in real time.

Requirements for the Highway Pilot include lane-level accuracy and a front-looking field of view (FOV) of 10° to 30° looking at the immediate path ahead. The Urban Pilot requires a wider field of view for short and mid-range objects with 360° coverage, with corner radars offering up to 150° FOV to detect cross-traffic and pedestrians.

The Urban Pilot also requires higher angular resolution to separate closely located objects in dense, low-speed traffic. In both cases, the expectations for the number of imaging radars on a vehicle and their performance will increase in the future (Figure 2).

Summary

4D imaging radar provides high-resolution images used in a variety of applications from automotive ADAS for autonomous driving to drones, industrial process control, and robotics. It can use multiple radars to provide more complete situational awareness, and performance demands are increasing over time, while costs are expected to decline.

References

4D Imaging High-Resolution Radar, Explained., Arbe Robotics
4D Millimeter-Wave Radar in Autonomous Driving: A Survey, arXiv
Achieving 4D radar imaging with a single-chip, 8-by-8 , cascadable transceiver, Texas Instruments
From Weather to Workzones: Expanding the Use of 4D Imaging Radar, Bitsensing
LiDAR vs RADAR: How 4D Imaging RADARs and FMCW LiDARs disrupt the Autonomous Tech Industry, Think Autonomous
Simulate an Automotive 4D Imaging MIMO Radar, MathWorks
The Evolution of 4D Imaging Radar: Unlocking the Future of Autonomous Driving, NXP
What Is 4D Imaging Radar?, Aptiv
What Is 4D Imaging Radar for ADAS and ITS?, Zilai Technology

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The post How does 4D imaging work with radar sensors? appeared first on Sensor Tips.

Basic in-cabin sensing for applications like occupant monitoring and child presence detection (CPD) previously relied on a variety of sensors including weight sensors, ultrasonic devices and simple ultrawide band (UWB) wireless sensors with a resolution of 10 to 3 cm. Modern in-cabin sensor systems use a single radar sensor to support multiple functions and deliver superior performance (Figure 1).

One of the factors driving the use of advanced sensor technologies are increasingly demanding safety standards from the European New Car Assessment Programme (Euro NCAP), the National Highway Traffic Safety Administration (NHTSA), and New Car Assessment Program (NCAP) in the U.S. That’s resulting in the development of more advanced sensors.

Common applications for radar sensors include (Figure 2):

• Child presence detection (CPD) sends an alert if a child or pet is left alone in the car.
• Advanced seatbelt reminder (SBR) detects seat occupancy without weight sensors.
• Smart airbag deployment is used to change airbag force based on occupant size and position.
• Vital signs monitoring can detect driver fatigue.
• Gesture controls are used primarily with the infotainment system to reduce driver distractions.

Architecture choices

Some of the considerations when designing in-cabin radar sensors include frequency selection, sensor placement, and the use of integrated sensors versus streaming data to a central electronic control unit (ECU).

60 GHz is currently the preferred choice. It has largely replaced 24 GHz since the higher frequency improves resolution (down to 5 cm) for distinguishing between adults and children and can accurately monitor vital signs. Compared with 24 GHz solutions, 60 GHz radar provides over 20x higher resolution due to a wider bandwidth (up to 5.5 GHz). Using advanced sensing algorithms, 60 GHz can detect sub-millimeter micro-movements, making it capable of sensing human breathing and even heartbeats.

Another choice is 77 GHz (76-81 GHz) that can provide even better resolution and angular accuracy but is currently used primarily in external advanced driver assistance system (ADAS) applications.

Placement of the sensor can be application dependent. Putting the sensor overhead in the headliner is most common and enables a single sensor to monitor the entire cabin and perform a variety of functions. Side mounting in the B-pillar is used for targeted occupant detection and for gesture controls. Vital signs can be monitored and occupants classified using under seat or dashboard mounted sensors.

The choice of physical location includes edge and satellite architecture. In an edge architecture, an intelligent sensor has integrated processing that sends finalized detection data to the ADAS ECU. Satellite architectures, a type of zonal architecture, uses a simpler and lower cost sensor and sends unprocessed data to a centralized ECU over a high-speed Ethernet connection.

Why not IR or RGB?

Infrared (IR) and visible light (RGB) imaging are also options for in-cabin applications, especially for driver alertness monitoring. They can be used to complement radar but are not generally considered to be substitutes for radar. It’s about more than imaging.

IR and RGB can provide high-resolution visual details like facial expressions and eye tracking that are useful for driver alertness monitoring, radar can support privacy protection while tracking vital signs.

IR imaging typically includes an IR lighting source and provides superior low-light performance but can be subject to interference under bright daylight conditions (Figure 3). RGB imaging can provide better context in daylight but has limitations under low-light or nighttime conditions, without introducing a light source that can be distracting to the driver.  

Engineers have developed sensors that capture both spectrums and that combine IR and RGB imaging, enabling systems that use RGB capability during daylight conditions and IR sensing when operating under lowlight or nighttime conditions. That can provide an option to radar for specific use cases, but radar supports the widest range of sensing requirements including CPD, SBR, gesture controls, and so on.

Summary

Automotive in-cabin radar monitors vehicle interiors, detecting, locating, and classifying occupants. These systems can detect micro-movements like breathing and heart rates through blankets or clothing and can provide CPD, SBR, airbag performance optimization, gesture controls and other functions. IR and RGB imaging can be used in certain cases, but are not generally considered to be substitutes for in-cabin radar.

References

3 Ways Radar Technology Is Changing the In-cabin Sensing Market, Texas Instruments
Automotive In-Cabin Radar Uncovered: The Essential Guide to Choose the Perfect Sensing Technology for Your Vehicle, IEEE
Automotive In-Cabin Sensing Monitoring with Infineon 60 GHz Radar, Infineon
In-Cabin Sensing System, NXP
In-cabin Sensor Advancements: Radar or 3D Cameras?, Edge AI + Vision Alliance
InCabin Radar Monitoring, Innosent
Interior Radar Based occupant monitoring system, Valeo
Overview of Child Presence Detection in Vehicle Safety, UniMax
Protecting the Smallest Passengers with Child Presence Detection Technology, AI Think
Radar Sensor Enhances In-Cabin Safety System Design, Lisleapex Electronic
Scaling Radar-Based In-Cabin Monitoring: Why Synthetic Data Is Essential for AI Teams, Anyverse

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The post How is radar used for automotive in-cabin sensing? appeared first on Sensor Tips.

On the input side, MEMS (Micro-Electro-Mechanical-Systems) microphones have already transformed how devices capture sound – replacing bulky, archaic electret capsules with tiny, high-performance silicon components that are now standard in virtually every smartphone, earbud, and smart speaker on the market today. The same transformation is now underway on the output side. The demand for compact, high-fidelity speakers, capable of delivering sound without the physical presence of traditional drivers, is accelerating across product categories: wearables that need full-range audio from invisible components, smart home devices where sound must be heard but the hardware unseen, and automotive interiors where speakers consume space and add weight that designers can no longer afford.

MEMS speakers are at the forefront of this shift, poised to do for sound output what MEMS microphones did for sound input: enable a new class of audio components that are smaller, more efficient, and natively compatible with semiconductor manufacturing. However, not all MEMS speaker architectures are created equal. As the technology matures, engineers and product developers need to understand the capabilities and limitations of the options available before committing to a platform.

This article explores the key technical considerations when selecting a MEMS speaker and examines how modulated ultrasound technology is expanding what these components can achieve.

First-generation MEMS speakers: tweeters

The first MEMS speakers to reach the market were effectively ‘tweeters’ – drivers optimized for high-frequency reproduction, typically above 2 kHz. Using small silicon diaphragms, these devices offered clear treble output in a form factor compact enough to fit inside TWS earbuds, augmenting the high-frequency performance of conventional balanced-armature or dynamic drivers in hybrid configurations. As a tweeter, this approach works, but challenges arise when trying to extend these devices to full-range audio.

The physics are straightforward: acoustic output is proportional to the volume of air displaced, the output of membrane area, and excursion. At the MEMS scale, both are severely constrained. A small diaphragm moving a few micrometers cannot push enough air to reproduce midrange and bass frequencies at useful sound pressure levels. As a result, the usable frequency range stays confined to the upper spectrum, output volume is capped by limited excursion, and delivering full-range audio still requires conventional drivers – meaning added bulk and complexity, which these MEMS ‘tweeters’ were meant to eliminate.

First-generation MEMS tweeters proved that silicon-based audio transducers could be manufactured at scale, but their performance is bound by membrane-displacement physics. Reaching full-range audio from a MEMS device requires a fundamentally different approach.

The shift to modulated ultrasound

First-generation MEMS tweeters generate sound the same way conventional speakers do – by moving a membrane at audible frequencies. Modulated ultrasound, however, takes an entirely different approach. Instead of moving a small membrane slowly, it moves the membrane very quickly—at ultrasonic frequencies in the hundreds of kHz range—functioning as a high-speed air pump. The audio signal is encoded as amplitude modulation of this carrier, and because the pump cycles hundreds of thousands of times per second, it displaces far more air per unit time than a membrane oscillating at audio frequencies – effectively trading membrane size for pump speed.

A critical distinction: the demodulation – the conversion from modulated ultrasound back to audible sound – occurs locally at each membrane, through the membrane’s own mechanical nonlinearity. This is not a parametric speaker effect, where ultrasonic beams interact in air to produce a narrow, directional audio beam. The output is omnidirectional, radiating sound like any conventional driver. The difference is in how the air displacement is generated, not in how the sound propagates.

This architecture unlocks a set of capabilities that are inaccessible to direct-radiating MEMS tweeters:

• Full-range audio: bass, midrange, and treble are produced from a single transducer, with no auxiliary drivers required. The modulated ultrasound device is a complete speaker, not a tweeter supplement.
• High SPL from a micro-scale source: the high-speed pumping mechanism achieves sound pressure levels that a comparably sized direct-radiating membrane cannot, enabling usable volume in open-ear and far-field applications.
• Vibration-free operation: minimal per-cycle excursion at ultrasonic frequencies eliminates the mechanical vibration inherent to direct-radiating drivers – with significant implications for component integration.
• Chip-scale integration: the transducer, ASIC, and algorithms can be co-packaged into a single module, enabling compact, multifunctional designs for next-generation wearables and ultra-thin devices.
• Power efficiency: low-voltage ultrasonic actuation keeps power consumption within the budget of battery-powered wearables and always-on AI assistants.

Modulated ultrasound removes the dependence on membrane area for air displacement, a significant constraint that made tweeters a partial solution, and replaces it with a mechanism that scales with speed rather than size.

Technical considerations when selecting a MEMS speaker

If a product designer’s goal is to augment an existing speaker – by adding high-frequency extension with a modest increase in size and power, for example – then a MEMS tweeter is a straightforward and well-proven solution. The selection criteria is conventional: frequency response, sensitivity, and mechanical compatibility with the existing driver.

When the goal is to replace an existing speaker entirely with a MEMS device, the evaluation becomes more nuanced. The critical considerations begin with the underlying transducer technology and extend to the specific demands of the target application.

Actuation technology: electrostatic vs. piezoelectric

MEMS speakers today are built on one of two actuation platforms: electrostatic or piezoelectric. The choice has significant implications for drive efficiency and system design. Electrostatic transducers present roughly 1,000 times less capacitance than their piezoelectric counterparts, which translates directly into lower drive currents and more efficient amplifier designs – a meaningful advantage in battery-powered devices where every milliwatt matters. Electrostatic platforms also build on the same proven silicon fabrication processes that underpin MEMS microphones, leveraging decades of manufacturing maturity, yield optimization, and supply chain infrastructure. Piezoelectric platforms potentially provide larger displacements for lower voltages, but depending on architecture, this does not always translate into more SPL or power-efficient operation.  System engineers should carefully evaluate the impact of drive capacitance on amplifier power, thermal management, reliability, RoHS compliance, and overall efficiency before committing to a platform.

In-Ear and near-ear applications

In-ear and near-ear devices—TWS earbuds, hearing aids, open-ear wearables—operate in tightly constrained acoustic environments. The speaker is coupled to the ear through small front cavities, narrow acoustic tubes, and fine meshes that present significant acoustic loads. In this context, the ability to shape acoustic resonances is a critical selection criterion. The speaker must maintain its output and fidelity under high acoustic impedance conditions; a transducer that performs well on the bench but loses output or distorts when loaded by a tight channel and mesh is unusable in a real product.

Some modulated ultrasound architectures are inherently more robust under high acoustic loads than direct-radiating designs. Their pumping mechanism can sustain output into small front cavities and restrictive acoustic paths without the output loss that a conventional membrane experiences when backloaded. Engineers evaluating MEMS speakers for in-ear and near-ear applications should test performance under realistic acoustic loading—not just in free-field or standard coupler conditions.

Free-field applications

In free-field applications – smart glasses, smart home devices, automotive surfaces – the design challenges shift. All small speakers struggle with low-frequency output in open acoustic environments. Here, the robustness of modulated ultrasound speakers to high acoustic loads becomes an advantage for a different reason: it enables acoustic design techniques that augment low-frequency performance, boosting output precisely where small transducers fall short.

Modulated ultrasound speakers can also deliver capabilities beyond conventional audio. Their bandwidth typically extends to 100 kHz and beyond, supporting ultrasonic sensing and communication alongside audio playback. And because they function as an air pump, they can even provide active cooling – moving air across heat-generating components as a micro-fan. However, added functionality comes at the cost of power, and a careful system-level assessment is needed to ensure that these features do not drain the battery budget that the speaker shares with the rest of the device.

Beamforming and speaker arrays

The area where free-field modulated ultrasound speakers truly differentiate is beamforming. Like their MEMS microphone counterparts, MEMS speakers offer the unit-to-unit uniformity and compact form factor needed to build speaker arrays that control the spatial delivery of sound, directing audio where it is needed and maintaining quiet zones where it is not. This opens the door to applications that are impractical with conventional speakers: personal audio zones in shared spaces, targeted notifications in automotive cabins, and directional sound in smart home environments, all while minimizing noise pollution.

Deploying speaker arrays efficiently may require the adoption of emerging audio interconnect protocols such as S3IS, which are designed for scalable, low-latency distribution of audio signals across multiple transducers. The engineering consideration here is systemic: speakers, amplifiers, and controllers must be architected together, and the choice of MEMS speaker platform directly affects how efficiently this system can be deployed and scaled.

Reliability and compliance

Reliability is a baseline requirement for any component going into a consumer product, and here MEMS speakers benefit from the same structural advantages that made MEMS microphones a trusted component across the industry. Solid-state silicon transducers with no moving coils, magnets, or adhesive bonds offer inherent robustness against moisture, dust, shock, and vibration, supporting IP67-rated product designs.

One issue that deserves attention is RoHS compliance. Some MEMS speaker architectures use materials that currently fall under RoHS exemptions. While these exemptions permit use today, they have a defined expiration horizon and can affect the product’s regulatory lifetime and end-of-life planning. Engineers should verify full RoHS compliance – not just exemption-based compliance – when selecting a speaker platform for products with multi-year production roadmaps.

Component integration: adding functionality without size

Conventional speakers vibrate; that is how they produce sound. In wearables, this vibration is felt directly by the user, causing discomfort during extended wear. In larger devices, it can excite enclosure resonances, rattle adjacent components, and create unwanted acoustic artifacts. Critically, for system design, vibration makes it impossible to co-locate a speaker with vibration-sensitive components like microphones and inertial sensors without introducing mechanical crosstalk that degrades their performance.

Modulated ultrasound speakers operate without the low-frequency mechanical vibration that characterizes direct-radiating drivers. The membrane moves at ultrasonic frequencies with minimal excursion per cycle, producing no perceptible vibration at the device level. This is not just a comfort feature — it is a system architecture enabler.

Without vibration isolation constraints, speakers, microphones, and other sensors can be integrated into the same package. A single chip-scale module can combine audio output, audio input, and environmental sensing in a footprint that would otherwise accommodate only one of these functions. For product developers, this means adding capability. Such as always-on voice pickup, active noise cancellation, acoustic echo cancellation, and spatial awareness – without adding size. In devices where every cubic millimeter is contested between batteries, antennas, and processing silicon, this kind of functional density is a decisive advantage.

Furthermore, the integration benefit extends beyond the package itself. When the speaker does not vibrate the enclosure, the acoustic design of the entire device becomes simpler. There is no need for mechanical decoupling structures, vibration-damping gaskets, or physical separation between the speaker and sensitive components. The result is fewer parts, a simpler assembly process, and more design freedom for the product team.

Conclusion

The audio industry is at an inflection point. The convergence of physical AI, always-on voice interfaces, and shrinking device form factors is creating an increased demand for speakers that can deliver full-range, high-fidelity sound from components that are essentially invisible. MEMS microphones showed us that semiconductor-native audio transducers could displace legacy technology at scale, and MEMS speakers are now following the same trajectory.

For engineers and product developers, the choice of MEMS speaker architecture is not a minor component decision; it shapes what the product can do. A tweeter augments an existing audio chain, while a modulated ultrasound speaker replaces it, opening the door to capabilities that conventional drivers cannot offer, including full-range audio from a chip-scale source, vibration-free operation that enables multi-sensor integration, acoustic load robustness for demanding in-ear and free-field designs, bandwidth extending well beyond human hearing, and the unit-to-unit uniformity needed for beamforming arrays.

The question is no longer whether MEMS speakers are ready for consumer products; it is which architecture matches the ambition of the product being designed.

The post What to know when choosing a MEMS speaker: unlocking performance with modulated ultrasound appeared first on Sensor Tips.

Physics of sub-millimeter detection

The appeal of 60 GHz radar (part of the millimeter-wave (mmWave) spectrum) is not just that it is contact-free. It is that the band supports high-resolution motion detection in a compact form factor. With a wavelength of roughly 5 mm, a 60 GHz system can resolve sub-millimeter movement through phase changes in the reflected signal. Specifically, a chest displacement of just 0.25 mm corresponds to a 36-degree phase rotation, yielding a high signal-to-noise ratio for physiological tracking.

This capability is particularly relevant because many traditional monitoring methods are still either intermittent or intrusive. Clinical systems provide accuracy but depend on wired sensors and patient compliance. Wearables improve convenience but require constant charging and user acceptance. Radar offers a third way: passive, continuous sensing that operates entirely in the background. This is especially valuable in sleep monitoring and elder care, where it can provide long-term data on breathing patterns and restfulness while preserving privacy better than camera-based systems.

Navigating signal processing hurdles

Despite its promise, 60 GHz radar is not a plug-and-play solution for medical monitoring. The engineering challenge lies in signal extraction, not basic detection. Respiration is relatively easy to sense because the motion amplitude is large (millimeter scale) and the frequency is low. Heartbeat is significantly harder. The chest displacement associated with cardiac motion is much smaller and easily obscured by respiratory harmonics and intermodulation products.

In a practical engineering environment, robust performance depends almost entirely on the signal-processing pipeline. Engineers must implement static clutter removal (to ignore room reflections), I/Q mismatch compensation, and adaptive filters to isolate the cardiac signal from the respiratory “noise.” Furthermore, random body movements, such as a patient rolling over, can momentarily swamp the micro-motion signatures. Modern systems handle this by “gating” the data, which involves temporarily pausing vital-sign extraction during high-motion intervals to prevent false readings.

As antenna integration and digital signal processing (DSP) continue to advance, 60 GHz systems are becoming more spatially selective. Compact arrays now enable beamforming, allowing the radar to isolate a specific subject even when multiple people or moving objects are present in the room. While 60 GHz radar won’t immediately replace gold-standard medical instruments, its role as a tool for continuous, unobtrusive monitoring, especially for respiratory rate and sleep-related trends, is set to expand across the digital health landscape.

Editor’s note: This is an updated version of Can 60 GHz radar sensing change healthcare monitoring?

The post Why 60 GHz radar is finding a place in health monitoring appeared first on Sensor Tips.

This FAQ examines how these factors affect sensing integrity and provides strategies for system design.

Q: How does high-voltage EMI filtering affect ac voltage sensing accuracy?
A: In 800 V systems, EMI filtering is necessary to meet regulatory standards. Designers typically implement large Y-capacitors, sometimes reaching 100 nF, between high-voltage lines and protective earth. These components introduce a trade-off regarding ac voltage sensing accuracy due to the time constant they insert into the measurement loop.

Data from Figure 1 identify two primary error mechanisms:

1. Transient-state settling errors: During a transient event or at the start of a measurement cycle, the Y-capacitor must charge through the sensing resistor. This results in a settling time delay. If the system relies on rapid insulation monitoring, this delay can prevent the sensor from reading the true voltage in a timely manner.
2. Steady-state phase displacement: Because Y-capacitors have frequency-dependent impedance, they introduce a phase shift. As shown in Figure 1, the sensed voltage waveform leads the true voltage, creating a phase delay that can compromise the accuracy of insulation resistance calculations.

To improve accuracy, the resistance in the measurement network should be reduced. Lowering the resistance shortens the charge and discharge periods, which minimizes phase deviation and settling time.

Q: How do high dv/dt nodes dictate the physical placement of sensors?
A: WBG devices, such as SiC MOSFETs, switch at high speeds, generating common-mode noise due to high dv/dt. In these systems, physical placement must account for electromagnetic coupling between power loops and sensitive signal traces.

Figure 2 maps specific high dv/dt switching nodes within bidirectional ac/dc and dc/dc converters. These nodes act as sources of capacitive noise coupling.

Placement guidelines:

• Separation: Sensitive sensing signals, gate loops, and input/output connectors should not be placed near high dv/dt switching nodes.
• Shielding: In high-density designs where physical separation is limited, grounded heat sinks can be utilized as physical shields. These shields intercept capacitive noise before it reaches the sensing circuitry.

Q: How can PCB layout geometry mitigate high di/dt magnetic coupling?
A: GaN devices exhibit fast turn-on transients, often exceeding 100 kV/μs, which generate high di/dt changes. These changes cause magnetic coupling that can penetrate sensor isolation barriers. This results in radio frequency interference that rectifies within operational amplifiers, manifesting as a dc shift in the output signal.

Figure 3 provides a comparison of layout techniques:

• Figure 3a shows significant current sensor distortion caused by a layout with large, horizontal signal and ground loops.
• Figure 3b demonstrates clean waveforms achieved through an optimized physical layout.

Designers should implement minimized, vertical multi-loop areas. By stacking VCC, signal, and ground traces vertically across the main power board and control board, the system maximizes negative mutual-partial inductance. This effect cancels out the external magnetic coupling from the power switching loop, ensuring measurement integrity despite high di/dt transients.

Summary

Designing for 800 V and WBG architectures requires balancing aggressive EMI filtering with sensor performance. Large Y-capacitors introduce phase displacement and lag that must be mitigated by lowering sensing resistance.

Physical layout is equally important, as designers must isolate sensors from high dv/dt switching nodes or deploy grounded heat sinks for shielding. For high di/dt environments, implementing vertical, tightly stacked PCB multi-loops maximizes negative mutual inductance to cancel magnetic noise.

References

Impacts of High Frequency, High di/dt, dv/dt Environment on Sensing Quality of GaN Based Converters and Their Mitigation, IEEE
A Comparative Analysis of Insulation Monitoring Device (IMD) Architectures in Bidirectional Onboard Chargers, Texas Instruments
Mitigating EMI with SiC Solutions in Renewable Energy & Grid-Connected Power Converters, Wolfspeed

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The post Increased voltage levels and faster switching speeds influence on sensor placement and accuracy in EV systems appeared first on Sensor Tips.

Power management

Smartwatches, earbuds, and health monitor wearables typically operate on 300 to 1500 mWh batteries. For these and other wearable products, power management ICs (PMICs) must address challenges such as inefficient power conversion, limited battery life, and space constraints. In addition, many wearables require multiple voltage regulators to provide a variety of voltages.

Using a design technique called Single-Inductor Multiple-Output (SIMO) technology, a PMIC can have one shared inductor generate multiple independent and independently regulated output voltages from a single input. For example, one company used this approach in a PMIC designed specifically for wearables and ultra-portable devices. With its single inductor, it delivers three outputs at 91% efficiency and replaces three traditional converters and inductors in a 19 mm² footprint that is 50% smaller than the alternative.

As part of power management, two subcategories deserve special consideration: charging and energy harvesting.

Charging/energy harvesting

Wearables have unique design requirements for charging based on their small size that pose problems for physical connectors. The solution is wireless charging, and available design techniques provide choices for implementing it. As shown in Table 1, a variety of factors enter into the choice of the right one. The wireless connectivity is provided by inductive, magnetic resonance, and RF (electromagnetic) techniques.

For wireless charging, industry standards have been developed by the Wireless Power Consortium and the NFC Forum.  Leveraging the existing Near Field Communication (NFC) antenna for both communication and power transfer, the NFC Wireless Charging Specification (NFC WLC) can deliver low-power charging—up to 1 watt—over distances of about 2 cm. NFC charging relies on a small antenna that already exists in billions of devices for connectivity and authentication. In contrast, inductive or resonance systems require relatively larger coils/antennas.

The Wireless Power Consortium (WPC) created its Qi Standard strictly for wireless power transfer (WPT).

Originally an exclusive feature on the 2020 iPhone 12 line, after five years, key parts of Apple’s MagSafe wireless charging are now also available on Android phones because Apple allowed their incorporation into WPC’s open Qi2.2 standard.

Far-field wireless charging, where wearables are charged from across the room using radio waves, infrared light, or lasers, is a possible future approach for wearables.  Some startups have already demonstrated functional prototypes.

In addition to these established wireless techniques, researchers continue to investigate new approaches for wireless power transfer. For example, one group of university researchers used self-capacitance technology to remotely transfer wearer-generated power to the wearable or devices installed in hard-to-reach areas of the body. As shown in Figure 1, their system utilizes the self-capacitance of the person’s body to wirelessly transfer power from portions of the body that generate higher power density to energy-constrained wearable devices. Its inventors envision implementation in millimeter-scale wearables in the power range of ~10mW, in end-user applications such as:

• Hard-to-reach wearable technology (including smart contact lenses and mouth guards)
• Smart textiles and stitched sensors (sports performance monitors, flexible electronics/wearables)

Energy harvesting from different conversion technologies continues to be the design goal of many companies and researchers. The ultimate solution could be a combination of the two companies’ expertise. For example, working together, one company’s RF-based wireless charging technology combined with the other company’s energy harvesting technology that captures RF power promises to open up the market for wireless charging up to two meters away. The combined solution can be used for a diverse array of connected products for retail, industrial, and consumer applications, including ultra-small, location-flexible, RF charging for wearables, hearables, and low-power electronics.

Algorithms

As shown in Figure 2, one company has developed algorithms for wearables that use an Inertial Measurement Unit (IMU) input through a 9-axis sensor fusion algorithm based on accelerometer, gyroscope, magnetometer, and strong artificial intelligence (AI). It offers these algorithms to consumers for wearable and other applications. Specifically, the algorithms address fall monitoring, activity recognition, step counting, head posture control (left and right turn of head, nod, head shake), and 3D head tracking for an immersive audio experience.

By operating on most of the world’s well-known low-power microprocessors, single-chip and Bluetooth SoCs, the algorithm can collect large amounts of data based on multiple sensors and constantly improve results through machine learning to achieve higher accuracy and more stable motion recognition. Figure 3 shows the small footprint that can be achieved.

Other systems aspects

Other system design aspects that impact or promise to impact wearable products include the display, simulation technology, including digital twins, multi-physics, and more, to optimize board layout and address thermal limitations, faults, and more.

For more complete power-consuming consideration, the screens on displays/optical systems can consume 10 to 100 mW, while augmented reality (AR) optics/glasses often require <320 mW, and Global Positioning System (GPS) sensors can require 70 mW+ of power.

With general-purpose simulation software used in all fields of engineering, manufacturing, and scientific research, multi-physics system-level analysis allows designers to gain insights into how physics couplings influence overall system performance.

References

Revolutionizing Power Management For Wearable Power Management With SIMO Technology
Extending the Battery Life of Electronic Wearable Devices
Self-capacitance Power Transfer for Efficient Wireless Charging of Small Wearables | Washington University Office of Technology Management
Powering the Future of Wearables: Battery Charging Strategies
Wireless Charging 2.0: NFC Power for Wearables in 2026
Energous and Atmosic Achieve Industry First Interoperability Energy Harvesting Advancing Development of Wireless Charging Applications
CyweeMotion
The COMSOL Product Suite
What is Multiphysics?
Digital twin for personalized medicine development

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