Microelectronics world news

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STMicroelectronics debuts the tiny ISM6HG256X three-in-one motion sensor in a 2.5 × 3-mm package for data-hungry industrial IoT (IIoT) systems, while also supporting edge AI applications. The IMU sensor combines simultaneous low-g (±16 g) and high-g (±256 g) acceleration detection with a high-performance precision gyroscope for angular rate measurement, ensuring the detection from subtle motion or vibrations to severe shocks.

“By integrating an accelerometer with dual full-scale ranges, it eliminates the need for multiple sensors, simplifying system design and reducing overall complexity,” ST said.

The ISM6HG256X is suited for IIoT applications such as asset tracking, worker safety wearables, condition monitoring, robotics, factory automation, and black box event recording.

In addition, the embedded edge processing and self-configurability support real-time event detection and context-adaptive sensing, which are needed for asset tracking sensor nodes, wearable safety devices, continuous industrial equipment monitoring, and automated factory systems.

(Source: STMicroelectronics)

Key features of the MEMS motion sensor are the unique machine-learning core and finite state machine, together with adaptive self-configuration and sensor fusion low power (SFLP). In addition, thanks to the SFLP algorithm, 3D orientation tracking also is possible with a few µA of current consumption,  according to ST.

These features are designed to bring edge AI directly into the sensor to autonomously classify detected events, which supports real-time, low-latency performance, and ultra-low system power consumption.

The ISM6HG256X is available now in a surface-mount package that can withstand harsh industrial environments from -40°C to 105°C. Pricing starts at $4.27 for orders of 1,000 pieces from the eSTore and through distributors. It is part of ST’s longevity program, ensuring long-term availability of critical components for at least 10 years.

Also available to help with development are the new X-NUCLEO-IKS5A1 industrial expansion board with MEMS Studio design environment and software libraries, X-CUBE-MEMS1. These tools help implement functions such as high-g and low-g fusion, sensor fusion, context awareness, asset tracking, and calibration.

The ISM6HG256X will be showcased in a dedicated STM32 Summit Tech Dive, “From data to insight: build intelligent, low-power IoT solutions with ST smart sensors and STM32,” on November 20.

The post Dual-range motion sensor simplifies IIoT system designs appeared first on EDN.

As precise control of cabin airflow and temperature becomes more critical in vehicles to enhance passenger comfort as well as to support advanced thermal management systems, Melexis introduces the MLX81350 LIN motor driver for air conditioning (AC) flaps and automated air vents in electric vehicles (EVs). The MLX81350 delivers a balanced combination of performance, system integration, and cost efficiency to meet these requirements.

The fourth-generation automotive LIN motor driver, built on high-voltage silicon-on-insulator technology, delivers up to 5 W (0.5 A) per motor and provides quiet and efficient motor operation for air conditioning flap motors and electronic air vents.

(Source: Melexis)

In addition to flash programmability, Melexis said the MLX81350 offers high robustness and function density while reducing bill-of-materials complexity. It integrates both analog and digital circuitry, providing a single-chip solution that is fully compliant with industry-standard LIN 2.x/SAE J2602 and ISO 17987-4 specifications for LIN slave nodes.

The MLX81350 features a new software architecture that enhances performance and efficiency over the previous generation. This enhancement includes improved stall detection and the addition of sensorless, closed-loop field-oriented control. This enables smoother motor operation, lower current consumption, and reduced acoustic noise to better support automotive HVAC and thermal management applications, Melexis said.

However, the MLX81350 still maintains pin-to-pin compatibility with its predecessors for easier migration with existing designs.

The LIN motor driver offers lots of peripherals to support advanced motor control and system integration, including a configurable RC clock (24-40 MHz), four general-purpose I/Os (digital and analog), one high-voltage input, 5× 16-bit motor PWM timers, two 16-bit general timers, and a 13-bit ADC with <1.2 -µs conversion time across multiple channels, as well as UART, SPI, and I²C master or slave interfaces. The LIN interface enables seamless communication within vehicle networks, and provides built-in protection and diagnostic features, including over-current, over-voltage, and temperature shutdown, to ensure safe and reliable operation in demanding automotive environments.

The MLX81350 is designed according to ASIL B (ISO 26262) and offers flexible wake-up options via LIN, external pins, or an internal wake-up timer. Other features include a low standby current consumption (25 µA typ.; 50 µA max.) and internal voltage regulators that allow direct powering from the 12-V battery, supporting an operating voltage range of 5.5 V to 28 V.

The MLX81350 is available now. The automotive LIN motor driver is offered in SO-8 EP and QFN-24 packages.

The post LIN motor driver improves EV AC applications appeared first on EDN.

OKW can now supply its plastic enclosures with bespoke internal metal brackets and mounting plates for displays and other large components. The company’s METCASE metal enclosures division designs and manufactures the custom aluminum parts in-house.

(Source: OKW Enclosures Inc.)

One recent project of this type involved OKW’s CARRYTEC handheld enclosures. Two brackets fitted to the lid allowed a display to be flush mounted; a self-adhesive label covered the join between screen and case. Another mounting plate, fitted in the base, was designed to support a power supply.

Custom brackets and supports can be configured to fit existing PCB pillars in OKW’s standard plastic enclosures. Electronic components can then be installed on the brackets’ standoffs.

CARRYTEC (IP 54 optional) is ideal for medical and laboratory electronics, test/measurement, communications, mobile terminals, data collection, energy management, sensors, Industry 4.0, machine building, construction, agriculture and forestry.

The enclosures feature a robust integrated handle with a soft padded insert. They can accommodate screens from 8.4″ to 13.4″. Interfaces are protected by inset areas on the underside. A 5 × AA battery compartment can also be fitted (machining is required).

These housings can be specified in off-white (RAL 9002) ABS (UL 94 HB) or UV-stable lava ASA+PC (UL 94 V-0) in sizes S 8.74″ × 8.07″ × 3.15″, M 10.63″ × 9.72″ × 1.65/3.58″ and L 13.70″ ×11.93″ × 4.61″.

In addition to the custom metal brackets and mounting plates, other customizing services include machining, lacquering, printing, laser marking, decor foils, RFI/EMI shielding, and installation and assembly of accessories.

For more information, view the OKW website: https://www.okwenclosures.com/en/news/blog/BLG2510-metal-brackets-for-plastic-enclosures.htm

The post OKW’s plastic enclosures add new custom features appeared first on EDN.

It’s just a fact of life. A BJT wired in common emitter, even after compensating for the effects of device and temperature variations, still isn’t a perfect current source.

Wow the engineering world with your unique design: Design Ideas Submission Guide

One of the flaws in the ointment is the Early effect of collector voltage on collector current. It can sometimes be estimated from datasheet parameters if output admittance (hoe) is specified (Ee ~ hoe / test current). A representative value is 1% per volt. Figure 1 shows its mischief in action in the behavior of a simple current mirror, where:

I2 = I1(1 + Vcb/Va)
Va ~ 100v
Ierr = Vcb/Va ~ 1%/V

Figure 1 Current mirror without emitter degeneration.

If the two transistors are matched, I2 should equal I1. But instead, Q2’s collector current may increase by 1% per Vcb volt. A double-digit Vcb may create a double-digit percentage error. That would make for a rather foggy “mirror”!

Fortunately, a simple trick for mitigating Early is well known to skilled practitioners of our art. (Please see the footnote). Emitter degeneration is based on an effect that’s 4000 times stronger than the effect of Vcb on Ic. 

That’s the effect of Vbe on collector current, and it can easily reduce Ee by two orders of magnitude. Figure 2 shows how it works:

I2 ~ I1(1 + Vcb/Va) – (0.026R)(I2 – I1))
Ierr ~ (Vcb/Va)/(Vr/26mV + 1)

 

Figure 2 Current mirror with emitter degeneration

Equal resistors R added in series with both emitters will develop voltages Vr = I1*R and I2*R that will be equal if the currents are equal. But if the currents differ (e.g., because of Early), then a Vbe differential will appear…duh…

This is useful because the Vbe differential will oppose the initial current differential, and the effect is large, even if Vr is small. Figure 3 shows how dramatically this reduces Ierr.

Figure 3 A normalized Early effect (y-axis) versus emitter degeneration voltage Ve = Ia*R (x-axis). Note that just 50 mV reduces Early by 3:1. That’s indeed a “long way”!

 Footnote

One DI has an earlier conversation about current mirrors and the Early effect: “A two-way mirror—current mirror that is.” In the grand tradition of editor Aalyia’s DI kitchen, frequent and expert commentator Ashutosh suggested how emitter degeneration could improve performance:

asa70

May 27, 2025

Regarding degen, i’ve found that a half volt at say 1mA FS helps match the in and out currents much better even at a tenth of the current, even for totally randomly selected transistors. I suppose it is because the curves will be closer at smaller currents, so that even a 50 mV drop goes a long way

Ashutosh certainly nailed it!  50mV does go a long (3:1) way!

Stephen Woodward’s relationship with EDN’s DI column goes back quite a long way. Over 100 submissions have been accepted since his first contribution back in 1974.

Related Content

• A two-way mirror—current mirror that is
• A two-way Wilson current mirror
• Use a current mirror to control a power supply
• A comparison between mirrors and Hall-effect current sensings

The post A current mirror reduces Early effect appeared first on EDN.

Ascent Solar Technologies Inc of Thornton, CO, USA – which designs and makes lightweight, flexible copper indium gallium diselenide (CIGS) thin-film photovoltaic (PV) panels that can be integrated into consumer products, off-grid applications and aerospace applications – has signed a teaming agreement with CisLunar Industries of Loveland, CO, USA, which develops scalable hardware and embedded control software for power conversion and conditioning...

My dad built this into a Snap-On tool case back in the 80s. I'm currently working on a PCB design so he can put together a new one. submitted by /u/TheHighestFever [link] [comments]

Rohde & Schwarz has announced that this year’s Mobile Test Summit will be an online, multi-session event catering to two major time zones. Wireless communications professionals are invited to register for individual sessions on the Rohde & Schwarz website. The sessions will cover a wide range of critical industry topics: AI and machine learning in mobile networks, non-terrestrial networks (NTN) for mobile devices, the transition from 5G to 6G and the next generation of Wi-Fi.

• The first topic, AI and machine learning, will cover how AI and ML are changing mobile networks.
• The second topic is NTN, and its sessions will cover how the evolving NTN landscape enhances the mobile user experience and provides true global coverage for IoT devices.
• The third topic addresses the transition from 5G to 6G, with a focus being on XR applications in the 6G age, new device types and the rise of private 5G NR networks. Special focus will be on the impact on test and measurement as the industry evolves from 5G to 6G.
• The fourth topic covers the latest advancements in Wi-Fi 8 technology and how they elevate the mobile user experience.

Alexander Pabst, Vice President of Wireless Communications at Rohde & Schwarz, says: “As we mark the fifth anniversary of hosting our popular Mobile Test Summit, we’re excited to continue this open forum for the global wireless community to exchange ideas, share experiences and debate the technical and operational questions that will shape the future of connectivity. The virtual, multi‑session format makes it easy for professionals in the wireless ecosystem around the globe to participate in focused conversations and obtain actionable insights that help shape the industry’s future.”

The post Rohde & Schwarz Mobile Test Summit 2025 on the future of wireless communications appeared first on ELE Times.

Sivers Semiconductors AB of Kista, Sweden (which supplies RF beam-former ICs for SATCOMs and photonic lasers for AI data centers) has announced additional details on the strategic partnership with POET Technologies Inc of Toronto, Ontario, Canada, designer and developer of the POET Optical Interposer, photonic integrated circuits (PICs) and light sources for the hyperscale data-center, telecom and artificial intelligence (AI) markets...

Infineon and SolarEdge are partnering to advance the development of highly efficient next-generation Solid-State Transformer (SST) technology for AI and hyperscale data centres.
– The new SST is designed to enable direct medium-voltage to 800–1500V DC conversion with over 99% efficiency, reducing size, weight, and CO₂ footprint – The collaboration combines SolarEdge’s DC expertise with Infineon’s semiconductor innovation to support sustainable, scalable power infrastructure and further expansion into the AI data-centre market

Infineon Technologies AG and SolarEdge Technologies, Inc. announced a collaboration to advance SolarEdge’s Solid-State Transformer (SST) platform for next-generation AI and hyperscale data centres. The collaboration focuses on the joint design, optimization and validation of a modular 2-5 megawatt (MW) SST building block. It combines advanced silicon carbide (SiC) switching technology from Infineon with SolarEdge’s proven power-conversion and control topology set to deliver >99% efficiency, supporting the global shift towards high-efficiency, DC-based data centre infrastructure.

The Solid-State Transformer technology is well positioned to play a crucial role in future,
highly efficient 800 Volt direct current (VDC) AI data centre power architectures. The
technology enables end-to-end efficiency and offers several key advantages, including a
significant reduction of weight and size, a reduced CO₂ footprint, and accelerated
deployment of power distribution, among others, when connecting the public grid with data
centre power distribution. The SST under joint development will enable direct medium-
voltage (13.8–34.5 kV) to 800–1500 V DC conversion.

“Collaborations like this are key to enabling the next generation of 800 Volt DC data centre
power architectures and further driving decarbonization,” said Andreas Urschitz, Chief
Marketing Officer at Infineon. “With high-performance SiC technology from Infineon,
SolarEdge’s proven capabilities in power management and system optimization are
enhanced, creating a strong foundation for the efficient, scalable, and reliable infrastructure
demanded by AI-driven data centres.”

“The AI revolution is redefining power infrastructure,” said Shuki Nir, CEO of SolarEdge. “It
is essential that the data centre industry is equipped with solutions that deliver higher levels of efficiency and reliability. SolarEdge’s deep expertise in DC architecture uniquely positions us to lead this transformation. Collaborating with Infineon brings world-class semiconductor innovation to our efforts to build smarter, more efficient energy systems for the AI era.”

As AI infrastructure drives an unprecedented surge in global power demand, data centre
operators are seeking new ways to deliver more efficient, reliable, and sustainable power.
Building on more than 15 years of leadership in DC-coupled architecture and high-efficiency
power electronics, this development would enable SolarEdge to expand into the data-centre
market with solutions designed to optimize power distribution from the grid to the compute
rack. This optimization relies on the efficient conversion of power, a challenge that
semiconductor solutions from Infineon are addressing, enabling efficient power conversions
from grid to core (GPU). With a focus on delivering reliable and scalable power systems
based on all relevant semiconductor materials silicon (Si), silicon carbide and gallium nitride (GaN), Infineon is enabling reduced environmental footprint and lower operating costs for the AI data centre ecosystem.

The post Infineon and SolarEdge collaborate to advance high-efficiency power infrastructure for AI data centres appeared first on ELE Times.

Transforming a simple 10-kΩ NTC thermistor into a precise digital thermometer is a great example of mixed-signal design in action. Using a mixed-signal IC—AnalogPAK SLG47011—this design measures temperatures from 0.1°C to 99.9°C with impressive accuracy and efficiency.

SLG47011’s analog-to-digital converter (ADC) with programmable gain amplifier (PGA) captures precise voltage readings, while its memory table and width converter drive a 3-digit dynamic 7-segment display. Each digit lights up in rapid sequence, creating a stable indication for a user, a neat demonstration of efficient multiplexing.

Compact, flexible, and self-contained, this design shows how one device can seamlessly handle sensing, computation, and display control—no microcontroller required.

 

Operating principle

The circuit schematic of the thermometer with a 3-digit 7-segment indicator is shown in Figure 1.

Figure 1 The circuit schematic displays a thermometer with 3-digit 7-segment indicator. Source: Renesas

The VDIV = 1.8 V voltage is applied to PIN 7 through a resistive divider RT / (R + RT), where R = 5.6 kΩ. PIN 8 activates the first digit, while PIN 6 activates the second digit and decimal point. PIN 4 activates the third digit.

The signal from PIN 7 goes to the single-ended input of the PGA (buffer mode, mode #6) and then to ADC CH0 for further sampling. The allowable temperature range measured by the thermometer is 0.1°C to 99.9°C (or 273.25 K to 373.05 K).

The voltage (VIN) after the resistive divider is equal to:

The ADC converts this voltage to a 10-bit code using the formula:

Whereas,

• RT is the resistance of the NTC thermistor:
• R0 = 10,000 Ω is the resistance at ambient temperature T0 (25°C or 285.15 K)
• B = 4050 K is a constant of the thermistor
• VIN is the voltage on PIN 7
• 1024 represents the 10-bit resolution of the ADC (210)
• 1620 represents the internal Vref in mV
• VINdec is VIN in 10-bit decimal format

The maximum value of VINdec is 1023.

The NTC thermistor resistances for the minimum and maximum value of the temperature are calculated using equations below:

The maximum voltage after the resistive divider is

The minimum voltage after the resistive divider is

The relationship between the measured temperature and VIN for the applied parameters of the circuit is shown in Figure 2.

Figure 2 Graph shows the relationship between temperature and VIN.

Thermometer design

The GreenPAK IC-based thermometer design is shown in Figure 3. Download free Go Configure Software Hub to open the design file and see how the functionality is carried out.

Figure 3 Thermometer with 3-digit 7-segment indicator design is built around a mixed-signal IC. Source: Renesas

The SLG47011 mixed-signal IC contains a memory table macrocell that can hold 4096 12-bit words. This space is enough to store the values of each of the three indicator digits for each VINdec (1024 * 3 = 3072 values in total). In other words, the 3n word of the memory table corresponds to the first digit, the 3n + 1 to the second digit, and the 3n + 2 to the third digit of each corresponding T, where n = VINdec.

The ADC output value is sent to the MathCore macrocell, where it’s multiplied by three. This value is then used as a memory table address. Assuming that the ADC output is 1000, the MathCore output is 3000. This means that the memory table values at 3000, 3001, and 3002 addresses will be used and will correspond to the indicator’s first, second, and third digits accordingly.

Data from the MathCore output goes to the IN+ CH0 input of the multichannel DCMP macrocell. This data is compared with the data on the IN- CH0 input, which is taken from the Data Buffer0 output. Data Buffer0 stores the data from the CNT11/DLY11/FSM0 macrocell, which operates in Counter/FSM mode.

The Counter/FSM is reset to “1” when a HIGH signal from the ADC data-ready output arrives and starts counting upward. The multichannel DCMP OUT0 output is connected to the Keep input of CNT11/DLY11/FSM0. This means that when the CNT11/DLY11/FSM0 current value is equal to the MathCore output value, the DCMP OUT0 output is HIGH, and the Keep input of CNT11/DLY11/FSM0 is also HIGH, keeping the counted value for further addressing to the memory table.

At the same time, together with CNT11/DLY11/FSM0, the Memory Control Counter is counting upward from 0 and sets the memory table address.

Thus, when the ADC measures a certain voltage value, the previously described comparison operation will point to the corresponding voltage value stored in the memory table—three consecutively recorded digits, which are then dynamically displayed on the 7-segment display.

The memory table’s stored data then goes to the width converter macrocell, which converts the serial 12-bit input into a parallel 12-bit output (Table 1).

Table 1 The above data highlights width converter connections. Source: Renesas

The inverter enables the decimal point (DP) through PIN 16 based on state of 3-bit LUT0 (second digit).

To dynamically display the temperature, the digits will be ON sequentially with a period of 300 μs. The period is set by the CNT2/DLY2 macrocell (in Reset Counter Mode). The 3-bit LUT4 sets the clock of the width converter based on its synchronization with the CNT11/DLY11/FSM0 clock and the state of DCMP OUT0.

The P DLY, DFF8, and 3-bit LUT12 macrocells form a state counter for the Up/Down input of the Memory Control Counter macrocell based on the state of the second digit (falling edge on OUT2 of the width converter).

When the first digit is ON, the Memory Control Counter counts upward by 1; when the second digit is first set ON, the state counter is set to LOW, forcing the Memory Control Counter to count down, while it has already activated the third number. Therefore, the second number is activated again, and the state counter goes HIGH, forcing the Memory Control Counter count upward, while it has already activated the first digit. Thus, all three digits will be sequentially activated until there is a new measured value from the ADC macrocell.

CNT8/DLY8, CNT12/DLY12/FSM1, and 3-bit LUT7 are used to properly turn on the ADC after the first turn-on when POR arrives, as well as during further operation when the ADC is turned on and off. CNT12/DLY12/FSM1 provides a period of 1.68 s, which results in the thermometer value being updated every 1.68 s.

Memory table filling algorithm

The algorithm below is shown for a VDIV voltage of 1.8 V and a resistive divider of 5.6 kΩ and RT.

First, the resistance value of RT (Ω) at ambient temperature T is calculated using the formula:

Second, the value of the temperature t (°C) for a determined RT value is calculated by:

Then, the calculated t (°C) values are rounded to the first decimal point.

For each VINdec value, three values are assigned in the memory table as follows: each VINdec corresponds to three consecutive values in the memory table 3n, 3n + 1, and 3n + 2, where n = VINdec.

Three separate columns for each of the values of 3n, 3n + 1, and 3n + 2 should be created. They each correspond to the first, second, and third digits of the indicator, respectively. The first column is assigned to the first digit of the rounded t value. The second column is assigned to the second digit, and the third column is assigned to the third digit.

For each digit of each column, a 7-bit binary value is found (m11 – m5), corresponding to the activation of the corresponding digit of the 7-segment display (Table 2).

Table 2 The above data highlights the 7-segment code. Source: Renesas

When the measured tmeas temperature is in range 0.1°C > tmeas > 99.9°C, the 0 – L symbols should be displayed on the indicator. The third digit is not activated in this case.

The next step is to add 5 more bits (m4 – m0) to the right of this value to get a 12-bit number.

The ninth bit (m3) is responsible for turning on the first digit, the tenth bit (m2) is responsible for turning on the second digit, and the eleventh bit (m1) for the third digit. Since a 7-segment indicator with a common cathode is used, turning on the digit is done with a LOW level (0). Therefore, for the first column (with words of type 3n), the ninth bit (m3) will equal 0, while the tenth (m2) and the eleventh (m1) bits will equal 1.

For the second column (with words of type 3n + 1), the tenth bit (m2) will be equal to 0, while the ninth (m3) and eleventh (m1) bits will be equal to 1. For the third column (with words of type 3n + 2), the eleventh (m1) bit will be equal to 0, while the ninth (m3) and tenth (m2) bits will be equal to 1.

The twelfth bit (m0) is not used, so its value does not affect the design. The resulting 3072 binary 12-bit values must then be converted to hex.

The required values for the memory table are already determined, now they need to be sorted in ascending order of the Word index and inserted into the appropriate location in the software. For a better understanding of the connections between the memory table and the width converter, view Figure 4.

Figure 4 The above diagram highlights connections between the memory table and the width converter. Source: Renesas

Test results

Figure 5 shows the result of measuring a temperature of around 17°C with respect to data obtained by a multimeter thermocouple.

Figure 5 Temperature range is set up to room temperature of around 17°C. Source: Renesas

Figure 6 shows the result of measuring a temperature of around 59°C with respect to data obtained by a multimeter thermocouple.

Figure 6 The measurement results show a temperature of around 59°C. Source: Renesas

Figure 7 shows the result of measuring a temperature of around 70°C with respect to data obtained by multimeter thermocouple.

Figure 7 The measurement results show a temperature of around 70°C. Source: Renesas

The mixed-signal integration

This design illustrates a practical approach to implementing a compact digital thermometer using the SLG47011 mixed-signal chip. Its ADC with PGA enables precise indirect temperature measurement, while the memory table and width converter manage dynamic control of the 3-digit 7-segment indicator.

By adjusting the resistive divider and updating the memory table, engineers can easily redefine the measurement range to suit different applications. The result is a straightforward and flexible thermometer design that effectively demonstrates mixed-signal integration in practice.

Myron Rudysh is application engineer at Renesas Electronics.

Nazar Ftomyn is application engineer at Renesas Electronics.

Yaroslav Chornodolskyi is application engineer at Renesas Electronics.

Bohdan Kholod is senior product development engineer at Renesas Electronics.

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• Electronic Thermometer with CrowPanel Pico 2.8 Inch 320×240 TFT LCD

The post Designing a thermometer with a 3-digit 7-segment indicator appeared first on EDN.

Boise State University has been selected for a $1.5m Defense Established Program to Stimulate Competitive Research (DEPSCoR) award, which will expand its defense-relevant research infrastructure and advance innovation in materials designed for extreme environments. Overall, 30 DEPSCoR awards were granted US-wide this year. The award will fund new instrumentation, training and discovery efforts...

BluGlass Ltd of Silverwater, Australia — which develops and manufactures gallium nitride (GaN) blue laser diodes based on its proprietary low-temperature, low-hydrogen remote-plasma chemical vapor deposition (RPCVD) technology — has provided the following update and financial report for its fiscal first-quarter 2026 (to end-September 2025)...

The future of the global industry will be defined by the integration of AI with robotics and IoT technologies. AI-enabled industrial automation will transform manufacturing and logistics across automotive, semiconductors, batteries, and beyond. IDTechEx predicts that the global sensor market will reach $255 billion by 2036, with sensors for robotics, automation, and IoT poised as key growth markets.

From edge AI and IoT sensors for connected devices and equipment (Industry 4.0) to collaborative robots, or cobots (Industry 5.0), technology innovations are central to future industrial automation solutions. As industry megatrends and enabling technologies increasingly overlap, it’s worth evaluating the distinct value propositions of Industry 4.0 and Industry 5.0, as well as the roadmap for key product adoption in each.

Sensor and robotics technology roadmap for Industry 4.0 and Industry 5.0 (Source: IDTechEx) What are Industry 4.0 and Industry 5.0?

Industry 4.0 emerged in the 2010s with IoT and cloud computing, transforming traditionally logic-controlled automated production systems into smart factories. Miniaturized sensors and industrial robotics enable repetitive tasks to be automated in a controlled and predictable manner. IoT networking, cloud processing, and real-time data management unlock productivity gains in smart factories through efficiency improvements, downtime reductions, and optimized supply chain integration.

Industry 4.0 technologies have gained significant traction in many high-volume, low-mix product markets, including consumer electronics, automotive, logistics, and food and beverage. Industrial robots have been key to automation in many sectors, excelling at tasks such as material handling, palletizing, and quality inspection in manufacturing and assembly applications.

If Industry 4.0 is characterized by cyber-physical systems, then Industry 5.0 is all about human-robot collaboration. Collaborative and humanoid robots better accommodate changing tasks and facilitate safer, more natural interaction with human operators—areas where traditional robots struggle.

Cobots are designed to work closely with humans without the need for direct control. AI models trained on tailored, application-specific datasets are employed to make cobots fully autonomous, with self-learning and intelligent behaviors.

The distinction between Industry 4.0 and Industry 5.0 technologies is ambiguous, particularly as products in both categories increasingly integrate AI. Nevertheless, technology innovations continue to enable the next generation of Industry 4.0 and Industry 5.0 products.

Intelligent sensors for Industry 4.0

In 2025, the big trend within Industry 4.0 is moving from connected to intelligent industrial systems using AI. AI models built and trained on real operation data are being augmented into sensors and IoT solutions to automate decision-making and offer predictive functionality. Edge AI sensors, digital twinning, and smart wearable devices are all key enabling technologies promising to boost productivity.

Edge-AI-enabled sensors are hitting the market, employing on-board neural processor units with AI models to carry out data inference and prediction on endpoint devices. Edge AI cameras capable of image classification, segmentation, and object detection are being commercialized for machine vision applications. Sony’s IMX500 edge AI camera module has seen early adoption in retail, factory, and logistics markets, while Cognex’s AI-powered 3D vision system gains traction for in-line quality inspection in EV battery and PCB manufacturing.

With over 15% of production costs arising from equipment failure in many industries, edge AI sensors monitoring equipment performance and automating maintenance can mitigate risks. Analog Devices, STMicroelectronics, TDK, and Siemens all now offer in-sensor or co-packaged machine-learning vibration and temperature sensors for industrial predictive maintenance. Predictive maintenance has been slow to take off, however, with industrial equipment suppliers and infrastructure service providers (rail, wind, and marine assets) being early adopters.

Simulating and modeling industrial operational environments is becoming more feasible and valuable as sensor data volume grows. Digital twins can be built using camera and position sensor data collected on endpoint devices. Digital twins enable performance simulation and maintenance forecasting to maximize productivity and minimize operational downtime. Proof-of-concept use cases include remote equipment operation, digital staff training, and custom AI model development.

Beyond robotics and automation, industrial worker safety is still a challenge. The National Safety Council estimates that the total cost of U.S. work injuries was $177 billion in 2023, with high incident rates in construction, logistics, agriculture, and manufacturing industries.

Smart personal protection equipment with temperature, motion, and gas sensors can monitor worker activity and environmental conditions, giving managers oversight to ensure safety. Wearable IoT skin patches offering hydration and sweat analysis are also emerging in the mining and oil and gas industries, reducing risk by proactively addressing the physiological and cognitive effects of dehydration.

Human-robot collaboration for Industry 5.0

Industry 4.0 relies heavily on automation, making it ideal for high-volume, low-mix manufacturing. As the transition to Industry 5.0 takes place, warehouse operators are seeking greater flexibility in their supply chains to support low-volume, high-mix production.

A defining aspect of Industry 5.0 is human-robot collaboration, with cobots being a core component of this concept. Humanoid robots are also designed to work alongside humans, aligning them with Industry 5.0 principles. However, as of late 2025, their technology and safety standards are still developing, so in most factory settings, they are deployed with physical separation from human workers.

Ten-year humanoid robot hardware market forecast (2025–2035) (Source: IDTechEx)

Humanoid robots, widely perceived as embodied AI, are projected to grow rapidly over the next 10 years. IDTechEx forecasts that the humanoid robot hardware market is set to take off in 2026, growing to reach $25 billion by 2035. This surge is fueled by major players like Tesla and BYD, who plan a more than tenfold expansion in humanoid deployment in their factories between 2025 and 2026.

As of 2025, despite significant hype around humanoid robots, there are still limited real-world applications where they fit. Among industrial applications, the automotive and logistics sectors have attracted the most interest. In the short- to mid-term, the automotive industry is expected to lead humanoid adoption, driven by the historic success of automation, large-scale production demands, and stronger cost-negotiation power.

Lightweight and slow-moving cobots, designed to work next to human operators without physical separation, have also gained significant momentum in recent years. Cobots are ideal options for small and mid-sized enterprises due to their low cost, small footprint, ease of programming, flexibility, and low power consumption.

Cobots could tackle a key industry pain point: the risk of shutdown to entire production lines when a single industrial robot malfunctions, due to the need to ensure human operators can safely enter robot working zones for inspection. Cobots could be an ideal solution to mitigate this, as they can work closely and flexibly with human operators.

The most compelling application of cobots is in the automotive industry for assembly, welding, surface polishing, and screwing. Cobots are also attractive in high-mix, low-volume production industries such as food and beverage.

Limited technical capabilities and high costs currently restrict wider cobot adoption. However, alternative business models are emerging to address these challenges, including cobot-as-a-service and try-first-and-buy-later models.

Outlook for Industry X.0

AI, IoT, and robotics are mutually enabling technologies, with industrial automation applications positioned firmly within this nexus and poised to capitalize on advancements.

Key challenges for Industry X.0 technologies are long return-on-investment (ROI) timelines and bespoke application requirements. Industrial IoT sensor networks take an average of two years to generate returns, while humanoid robots in warehouses require 18 months of pilot testing before broader use. However, economies-of-scale cost reductions and supporting infrastructure can ease ROI concerns, while long-term productivity gains will also offset high upfront costs.

The next generation of industrial IoT technology will leverage AI to deliver productivity improvements through greater device intelligence and automated decision-making. With IDTechEx forecasting that humanoid and cobot adoption will take off by the end of the decade, the 2030s are set to be defined by Industry 5.0.

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FCC ID: 2AN9S-TPX00227 Arduino’s upcoming Nesso N1 has appeared in a recent FCC filing, offering one of the most detailed looks at the device so far. Although the board has been announced, it has not yet reached retail, and the filing confirms that development is nearing completion. The documents include complete schematics, which is uncommon and provides an unusually transparent view of the design. The Nesso N1 is based on an ESP32 C6 controller with support for Wi Fi, Bluetooth Low Energy, and LoRa at 915 MHz. It includes a 1.14 inch color touchscreen, detachable antennas, a BMI270 motion sensor, Grove and Qwiic expansion ports, and a built in 200 mAh battery for portable use. Internal and external photos show a compact layout focused on prototyping flexibility. submitted by /u/Electrical-Plum-751 [link] [comments]

Infineon Technologies AG of Munich, Germany and smart energy technology firm SolarEdge Technologies Inc of Milpitas, CA, USA are collaborating to advance SolarEdge’s solid-state transformer (SST) platform for next-generation AI and hyperscale data centers. The collaboration focuses on the joint design, optimization and validation of a modular 2–5MW SST building block. It combines Infineon’s silicon carbide (SiC) switching technology with SolarEdge’s proven power-conversion and control topology set to deliver >99% efficiency, supporting the global shift towards high-efficiency, DC-based data-center infrastructure...

Epitaxial deposition and process equipment maker Veeco Instruments Inc of Plainview, NY, USA has received an order for a Propel300 metal-organic chemical vapor deposition (MOCVD) system from a “major power semiconductor integrated device manufacturer” (IDM) for gallium nitride (GaN) epitaxy on 300mm silicon (Si) wafers...

A simple current source

It has long been known that the simple combination of a depletion-mode MOSFET (and before these were available, a JFET) and a resistor made a simple, serviceable current source such as that seen on the right side of Figure 1.

Figure 1 Current versus voltage characteristics of a DN2540 depletion mode MOSFET and the circuit of a simple current source made with one, both courtesy of Microchip.

Wow the engineering world with your unique design: Design Ideas Submission Guide

This is evident from the figure’s left side, which shows the drain current versus drain voltage characteristics for various gate-source voltages of a DN2540 MOSFET. Once the drain voltage rises above a certain point, further increases cause only very slight rises in drain current (not visible on this scale). This simple circuit might suffice for many applications, except for the fact that the VGS required for a specific drain current will vary over temperature and production lots. Something else is needed to produce a drain current with any degree of precision.

Alternative current source circuits

And so, we might turn to something like the circuits of Figure 2.

Figure 2 A current source with a more predictable current, left (IXYS) and a voltage regulator which could be employed as a current source with a more predictable current, right (TI). Source: IXYS and Texas Instruments

In these circuits, we see members of the ‘431 family regulating MOSFET source and BJT emitter voltages. The Texas Instruments circuit on the right demonstrates the need for an oscillation-prevention capacitor, and my experience has been that this is also needed with the IXYS circuit on the left.

Although RL1, RS, and R1 pass precise, well-regulated currents to the transistors in their respective circuits, resistors RB and R do not. RB’s current is subject to a not well-controlled VGS, and R’s is affected by whatever variations there might be in VBATT.

The MOSFET circuit is a true two-terminal current source, so a load can be connected in series with the current source at its positive or negative terminal. But then the load is always subjected to the poorly-controlled RB current.

The BJT is part of a three-terminal circuit, and for a load to avoid the VBATT-influenced current through R, it could only be connected between VBATT and the BJT collectors. Even so, variations in VBATT could produce currents, which lead to voltages that are not entirely rejected at the TLA431 cathode, and so would produce uncontrolled currents in the BJTs and therefore in the load.

A true two-terminal current source

Figure 3 addresses these limitations in circuit performance. In analyzing it, as always, I rely on datasheet maximum and minimum values whenever they are available, but resort to and state that I’m employing typical values when they are not.

Figure 3 This circuit delivers predictable currents to U1 and M1 and therefore to a load. It’s a true two-terminal current source which accommodates load connection to both low and high side.

U1 establishes 1.24 · ( 1 + R4 / R3 ) volts at VS and adds a current of VS / (R4 + R3) to the MOSFET drain.

An additional drain current comes from:

2 · ( VS – VBE(Q2) / ( R2 + R5 )

The “2” is due to the fact that R2 and R1 currents are identical (discounting the Early effect on Q1). The current through R1 is nearly constant regardless of the value of VGS. This current provides what U1 needs to operate.

The precision of the total DC current through the load is limited by the tolerances of R1 through R5, the U1 reference’s accuracy, and the value of the BJT’s temperature-dependent VBE drop. (U1’s maximum feedback reference current over its operating temperature is a negligible 1 µA.)

U1 requires a minimum of 100 µA to operate, so R5 is chosen to provide it with 150 µA. Per its On Semi datasheet, at this current and over Q1’s operating temperature range, the 2N3906’s typical VCE saturation voltage is 50 mV. Add that to the 15mV drop across R1 for a total of 65 mV, which is the smallest achievable VSG value.

Accordingly, we are some small but indeterminant amount shy of the maximum drain current guaranteed for the part (at 25°C, 25 V VDS, and 0 V VGS only) by its datasheet. At the other extreme, under otherwise identical conditions, a VGS of -3.5 V will guarantee a drain current of less than 10 µA. For such, U1 and the circuit as a whole will operate properly at a VS of 5 VDC.

Higher temperatures might require a more negative VGS by a maximum of -4.5 mV/°C and, therefore, possibly larger values of VS and, accordingly, of R5. This would be to ensure that U1’s cathode voltage remains above 1.24 V under all conditions.

D2 is selected for a Zener voltage which, when added to D1’s voltage drop, is greater than VS, but is less than the lesser of the maximum allowed cathode-anode voltage of U1 (18 V) and the maximum allowed VGS of M1 (20 V). D1‘s small capacitance shields the rest of the circuit from the Zener capacitance, which might otherwise induce oscillations. The diodes are probably not needed, but they provide cheap protection. Neither passes current or affects circuit performance during normal operation. C1 ensures stable operation.

U1 strives to establish a constant voltage at VS regardless of the DC and AC voltage variations of the unregulated supply V1. Working against it in descending order of impact are the magnitude of the conductance of the R3 + R4 resistor string, U1‘s falling loop gain with frequency, and M1’s large Rds and small Cds. Still, the circuit built around the 400-V VDS-capable M1 achieves some surprisingly good results in the test circuit of Figure 4.

Figure 4 Circuit used to test the impedance of the Figure 3 current source.

Table 1 and Figure 5 list and display some measurements. Impedances in megohms are calculated using the formula RLOAD · 10(-dB, VLOAD / VGEN) / 20 / 1E6.

Table 1 Impedances of the current source of Figure 3 at various frequencies, evaluated using the circuit of Figure 4.

Figure 5 Plotted curves of Figure 3 current source impedance from the data in Table 1.

Observations

There are several conclusions that can be drawn from the curves in Figure 5. The major one is that at low frequencies, the AC impedance Z is roughly inversely proportional to current. A more insightful way to express this is that Z is proportional to R3 + R4, which sets the current. With larger resistance, current variations produce larger voltages for the ‘431 IC to use for regulation; that is, there’s more gain available in the circuit’s feedback loop to increase impedance.

Another phenomenon is that in the 1 and 10-mA current curves, the impedance rises much more quickly as frequency increases above 1 kHz. This is consistent with the fact that the TLVH431B gain is more or less flat from DC to 1 kHz and falls thereafter. The following phenomenon masks this effect somewhat at the higher 100 mA current.

Finally, at all currents, there is an advantage to operating at higher values of VDS. This is especially apparent at the highest current, 100 mA. And this is consistent with the fact that for the characteristic curves of the DN2540 MOSFET seen in Figure 1, higher VDS voltages are required at higher currents before the curves become horizontal.

Precision current source

A precision high impedance, moderate-to high voltage-compliant current source has been introduced. Its two-terminal nature means that a load in series with it can be connected to the source’s positive or negative end. Unlike earlier designs, the ‘431 regulator IC’s operating current is independent of both the source’s supply voltage and of its MOSFET’s VGS voltage. The result is a more predictable DC current as well as higher AC impedances than would otherwise be obtainable.

Christopher Paul has worked in various engineering positions in the communications industry for over 40 years.

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Let us begin this session by revisiting a nostalgic motor control IC—the AN6651—designed for rotating speed control of compact DC motors used in tape recorders, record players, and similar devices.

The figure below shows the AN6651’s block diagram and a typical application circuit, both sourced from a 1997 Panasonic datasheet. These retouched visuals offer a glimpse into the IC’s internal architecture and its practical role in analog motor control.

Figure 1 Here is the block diagram and application circuit of the AN6651 motor control IC. Source: Panasonic

Luckily, for those still curious to give it a try, the UTC AN6651—today’s counterpart to the legacy AN6651—is readily available from several sources.

Before we dive deeper, here is a quick question—why did I choose to begin with the AN6651? It’s simply because this legacy chip elegantly controls motor speed using back electromotive force (EMF) feedback—a clever analog technique that keeps rotation stable without relying on external sensors.

In analog systems, this approach is especially elegant: the IC monitors the voltage generated by the motor itself (its back EMF), which is proportional to speed. By adjusting the drive current to maintain a target EMF, the chip effectively regulates motor speed under varying loads and supply conditions.

And yes, this post dives into back EMF (BEMF) and electric motors. Let’s get started.

Understanding back EMF in everyday motors

A spinning motor also acts like a generator, as its coils moving through magnetic fields induce an opposing voltage called back EMF. This back EMF reduces the current flowing through the motor once it’s up to speed.

At that point, only enough current flows to overcome friction and do useful work—far less than the surge needed to get it spinning. Actually, it takes very little time for the motor to reach operating speed—and for the current to drop from its high initial value.

This self-regulating behaviour of back EMF is central to motor efficiency and protection. As the mechanical load rises and the motor begins to slow, back EMF decreases, allowing more current to flow and generate the required torque. Under light or no-load conditions, the motor speeds up, increasing back EMF and limiting current draw.

This dynamic ensures that the motor adjusts its power consumption based on demand, preventing excessive current that could overheat the windings or damage components. In essence, back EMF reflects motor speed and actively stabilizes performance, a principle rooted in classical DC motor theory.

It ‘s worth noting that back EMF plays a critical role as a natural current limiter during normal motor operation. When motor speed drops—whether due to a brownout or excessive mechanical loading—the resulting reduction in back EMF allows more current to flow through the windings.

However, if left unchecked, this surge can lead to overheating and permanent damage. Maintaining adequate speed and load conditions helps preserve the protective function of back EMF, ensuring safe and efficient motor performance.

Armature feedback method in motion control

Armature feedback is a form of self-regulating (passive) speed control that uses back EMF and has been employed for decades in audio tape transport mechanisms, luxury toys, and other purpose-built devices. It remains widely used in low-cost motor control systems where precision sensors or encoders are impractical.

This approach leverages the motor’s ability to act as a generator: as the motor rotates, it produces a voltage proportional to its speed. Like any generator, the output also depends on the strength of the magnetic field flux.

Now let’s take a quick look at how to measure back EMF using a minimalist hardware setup.

Figure 2 The above blueprint presents a minimalist hardware setup for measuring the back EMF of a DC motor. Source: Author

Just to elaborate, when the MOSFET is ON, current flows from the power supply through the motor to ground, during which back EMF cannot be measured. When the MOSFET is OFF, the motor’s negative terminal floats, allowing back EMF to be measured. A microcontroller can generate the required PWM signal to drive the MOSFET.

Likewise, its onboard analog-to-digital converter (ADC) can measure the back EMF voltage relative to ground for further processing. Note that since the ADC measures voltage relative to ground, a lower input value corresponds to a higher back EMF.

That is, measuring the motor’s speed using back EMF involves two alternating steps: first, run the motor for a brief period; then, remove the drive signal. Due to inertia in the motor and mechanical system, the rotor continues to spin momentarily, and this coasting phase provides a window to sample the back EMF voltage and estimate the motor’s rotational speed.

The reference signal can then be routed to the PWM section, where the drive power is fine-tuned to maintain steady motor operation.

Still, in most cases, since the PWM driver outputs armature voltage as pulses, back EMF can also be measured during the intervals between those pulses. Keep note, when the transistor switches off, a strong inductive spike is generated, and the recirculation current flows through the antiparallel flyback diode. Therefore, a brief delay is demanded to allow the back EMF voltage to settle before measurement.

Notably, a high-side P-channel MOSFET can be used as a motor driver transistor instead of a low-side N-channel MOSFET. Likewise, discrete op-amps—rather than dedicated ICs—can also govern motor speed, but that is a topic for another day.

And while this is merely a blueprint, its flexibility allows it to be readily adapted for measuring back EMF—and thus the RPM—of nearly any DC motor. With just a few tweaks, this low-cost approach can be adapted to support a wide range of motor control applications—sensorless, scalable, and easy to implement. Naturally, it takes time, technical skill, and a bit of patience—but you can master it.

Back EMF and the BLDC motor

Back EMF in BLDC motors acts like a built-in feedback system, helping the motor regulate its speed, boost efficiency, and support smooth sensorless control. The shape of this feedback signal depends on how the motor is designed, with trapezoidal and sinusoidal waveforms being the most common.

While challenges like low-speed control and waveform distortion can arise, understanding and managing back EMF effectively opens the door to unlocking the full potential of BLDC motors in everything from fans to drones to electric vehicles.

So, what are the key effects of back EMF in BLDC motors? Let us take a closer look:

• Design influence: The shape of the back EMF waveform—trapezoidal or sinusoidal—directly affects control strategy, acoustic noise, and how smoothly the motor runs. Trapezoidal designs suit simpler, cost-effective controllers, while sinusoidal profiles offer quieter, more refined motion.
• Position estimation: Back EMF is widely used in sensorless control algorithms to estimate rotor position.
• Speed control: Back EMF is directly tied to rotor speed, making it a reliable signal for regulating motor speed without external sensors.
• Speed limitation: Back EMF eventually balances the supply voltage, limiting further acceleration unless voltage is increased.
• Current modulation: As the motor spins faster, back EMF increases, reducing the effective voltage across the windings and limiting current flow.
• Torque impact: Since back EMF opposes the applied voltage, it affects torque production. At high speeds, stronger back EMF draws less current, resulting in lower torque.
• Efficiency optimization: Aligning commutation with back EMF waveform improves performance and reduces losses.
• Regenerative braking: In some systems, back EMF is harnessed during braking to feed energy back into the power supply or battery, a valuable feature in electric vehicles and battery-powered devices where efficiency matters.

Oh, I nearly skipped over a few clever tricks that make BLDC motor control even more efficient. One of them is back EMF zero crossing—a sensorless technique where the controller detects when the voltage of an unpowered phase crosses zero, presenting it to time commutation events without physical sensors. To avoid false triggers from electrical noise or switching artifacts, this signal often needs debouncing, either through filtering or timing thresholds.

But this method does not work at startup, when the rotor is not spinning fast enough to generate usable back EMF. That is where open-loop acceleration comes in: the motor is driven with fixed timing until it reaches a speed where back EMF becomes detectable and closed-loop control can take over.

For smoother and more precise performance, field-oriented control (FOC) goes a step further. It transforms motor currents into a rotating reference frame, enabling accurate torque and flux control. Though traditionally used in permanent magnet synchronous motors (PMSMs), FOC is increasingly applied to sinusoidal BLDC motors for quieter, more refined motion.

A vast number of ICs nowadays make sensorless motor control feel like a walk in the park. As an example, below you will find the application schematic of the DRV10983 motor IC, which elegantly integrates power MOSFETs for driving a three-phase sensorless BLDC motor.

Figure 3 Application schematic of the DRV10983 chip, illustrating its function as a three-phase sensorless motor driver with integrated power MOSFETs. Source: Texas Instruments

That wrap up things for now. Talked too much, but there is plenty more to uncover. If this did not quench your thirst, stay tuned—more insights are brewing.

T. K. Hareendran is a self-taught electronics enthusiast with a strong passion for innovative circuit design and hands-on technology. He develops both experimental and practical electronic projects, documenting and sharing his work to support fellow tinkerers and learners. Beyond the workbench, he dedicates time to technical writing and hardware evaluations to contribute meaningfully to the maker community.

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