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Rohde & Schwarz has upgraded its industry leading R&S SMW200A vector signal generator and its midrange counterpart, the R&S SMM100A. With significant enhancements in error vector magnitude (EVM) performance, the evolved R&S SMW200A is a robust choice for both 5G NR FR3 research and high demand RF applications like power amplifier testing. The instrument now also includes a new RF linearization software option, which uses digital pre distortion to optimize EVM at high output power. The R&S SMM100A has also been upgraded with improved EVM capabilities.

Rohde & Schwarz has introduced the latest evolution of its two vector signal generators, the signature R&S SMW200A for the most demanding applications, and its midrange counterpart, the best-in-class R&S SMM100A. Besides a redesigned front panel and user interface, the R&S SMW200A has been equipped with modified microwave hardware for enhanced error vector magnitude (EVM) performance as well as higher output power in the frequency range above 20 GHz. This addresses the demands of 5G NR FR2 research and RF component and module testing.

This upgrade comes with an RF linearization software option R&S SMW-K575, which utilizes digital pre distortion technology to optimize EVM at high output power. This ensures high accuracy and stability, even for complex modulation schemes in the entire frequency range.

These key upgrades also extend to the R&S SMM100A, the midrange counterpart of the R&S SMW200A. The R&S SMM100A also comes with a new low phase noise option, R&S SMM B709. With this option, the R&S SMM100A can provide, for example, an EVM performance better than –53 dB for an IEEE802.11be signal with a bandwidth of 320 MHz.

Customers with previous models of the R&S SMW200A or R&S SMM100A can also benefit from the new performance enhancements offered by R&S SMx-K575 RF linearization: Rohde & Schwarz offers retrofit options through a simple service and calibration process.

Gerald Tietscher, Vice President of Signal Generators, Power Supplies and Meters at Rohde & Schwarz, says: “With increasing data rates and modulation scheme complexity, achieving low EVM is critical for ensuring stability and robustness in wireless connectivity applications. The latest evolution of our R&S SMW200A and R&S SMM100A vector signal generators is a testament to our commitment to making our art

of signal generation even better. With their superior RF characteristics and exceptional EVM performance, these instruments are a pivotal resource for handling the requirements of the most advanced test applications.”

The post New R&S SMW200A and R&S SMM100A vector signal generators feature significantly improved EVM performance appeared first on ELE Times.

Smart clothes, also known as e-textiles or wearable technology, are garments embedded with sensors, actuators, and other electronic components that enable them to interact with the wearer and environment. These clothes can monitor various health parameters, provide connectivity, and even adapt to the user’s needs.

How Do Smart Clothes Work?

Smart clothes work through integrated sensors and actuators that can detect physical movements, environmental factors, and biological signals. These sensors collect data such as heart rate, body temperature, moisture levels, posture, or even muscle activity. The data is then transmitted to a connected device (like a smartphone or cloud server) for analysis and real-time feedback. Smart fabrics may also have embedded conductive threads that allow them to transmit electrical signals.

Some smart clothes are powered by flexible batteries, solar cells, or energy harvested from movement (like piezoelectric materials), making them lightweight and functional.

Smart Clothes Technology

The core technologies in smart clothes include:

• Conductive fabrics and threads: Materials capable of transmitting electricity, enabling the integration of sensors and circuits into fabrics.
• Flexible sensors: Lightweight sensors that measure things like temperature, pressure, motion, and even muscle activity.
• Wireless communication: Bluetooth, NFC, or Wi-Fi to send data from the clothes to external devices.
• Power sources: Small batteries or energy-harvesting systems like solar cells or kinetic energy converters.

Smart Clothes Applications

1. Health and Fitness Monitoring: Smart clothes like heart rate-monitoring shirts, posture-correcting jackets, and smart sports bras help track and analyze physical activity, vital signs, and performance metrics in real-time.
2. Medical and Rehabilitation: Some garments are designed for patients, offering features like tracking vital signs, muscle movements, and even aiding muscle stimulation.
3. Safety: Smart clothes can include features like LED lights for better visibility for cyclists, workers, and runners, and GPS for tracking.
4. Fashion and Aesthetics: Garments with integrated displays that change patterns or colors based on the environment or user input.
5. Climate Control: Thermal adaptive clothing adjusts to body temperature, providing cooling or heating effects.
6. Workplace Use: In sectors like construction, smart clothing can alert workers about their posture, fatigue, or physical stress.

Smart Clothes Advantages

1. Health Monitoring: They enable continuous monitoring of health metrics like heart rate, blood pressure, and body temperature, which can be used for preventive health care.
2. Improved Performance: Athletes and fitness enthusiasts can track performance and adjust their training based on real-time data.
3. Enhanced Safety: In work environments, smart clothes can provide early warnings about hazardous conditions, track worker location, or improve visibility.
4. Personalized Comfort: With adaptive features, smart clothes can adjust their temperature, moisture level, or fit according to environmental conditions and personal preferences.
5. Convenience: The integration of technology into clothes reduces the need to carry separate gadgets and can be more discreet and comfortable compared to wearables like watches or fitness bands.

Smart clothes continue to evolve, combining the worlds of fashion, health, technology, and convenience into one seamless experience.

 

The post Smart Clothes Definition, Working, Technology & Applications appeared first on ELE Times.

Semiconductor innovations in battery systems are leading to energy storage adoption

Takeaways

• Power grids weren’t designed to handle new types of electricity demands and supplies.
• Battery energy storage systems are key to transforming and protecting the grid.
• Innovation in battery-management and high-voltage semiconductors help grids get the most out of battery storage.

The growing adoption of electric vehicles (EVs) and the transition to more renewable energy sources are reducing our more-than-century-long reliance on fossil fuels. Electric utilities are increasingly turning to solar panels and wind turbines rather than natural gas-fueled turbines to generate the electricity needed to charge EVs, as well as help power our homes and businesses. Together, these trends are poised to bring us closer to a sustainable energy future.

Those same trends also pose a big challenge to the electricity grid. Demand can vary throughout the day – and so can supplies of solar and wind energy based on changes in the weather. That’s why batteries are becoming an essential component of the grid.

“Batteries can fill in the gap when it’s cloudy and the wind dies down,” said Richard Zhang, a Virginia Tech professor who teaches power electronics and has worked in the grid and energy industry for 25 years. “And batteries improve the economics of electricity because they can be charged during off-peak times, providing electricity for charging EVs at peak times.”

Getting batteries to safely, reliably and cost-effectively store and release the large amounts of electricity running through the grid is a complex challenge. That’s where our company’s expertise in providing advanced battery-management semiconductor solutions can make a big difference.

“The bigger, higher-voltage batteries used in the grid require better thermal management and more precise monitoring,” said Samuel Wong, our company’s vice president and general manager of Battery Management Solutions. “Effectively managing those batteries requires understanding battery chemistry and adapting high-performance semiconductor devices to safely get the most out of each battery.”

Smoothing out the grid

The adoption of solar and wind generation and EVs is good news for the planet, Richard said. The problem is that power grids weren’t originally designed to handle these new types of electricity demands on available energy.

“Getting people to switch to EVs is easier today than it was just a few years ago,” he said. “Now the growing issue is getting the electricity infrastructure to handle them, alongside other energy demands.”

The challenge, Samuel said, is grid instability – in other words, fluctuations in electricity generation and usage. Variations in energy supply occur in solar and wind generation, especially the complete loss of solar power at night. Supply and demand swings may also occur from the charging routines of EV owners.

“If everyone comes home in the evening and plugs in their EVs for the night, the grid might not be able to handle it,” he said.

Samuel and Richard, like most power experts, agree on the solution to grid instability: energy storage systems (ESS). Storage systems – usually in the form of batteries – can capture and hold excess energy in the grid when supply is high and demand is low, and then make it available at other times. You may be picturing the relatively small, light battery cells used in EVs. But for the grid, an ESS might consist of a railroad-car-sized stack of bigger, heavier cells that each can operate at as much as 4 megawatt-hours (MWh) – enough energy to power thousands of homes.

Staging storage systems at different points in the grid optimizes their ability to distribute enormous amounts of electricity to neighborhoods when and where they’re needed. That can mean placing an ESS alongside a solar panel farm, where it can soak up the excess energy during the day and then pump it back out to the grid at night. Or, an ESS placed in a community can more easily grab energy from local rooftop solar panels and later supply the extra electricity needed to charge nearby EVs. “An ESS can serve as a local reservoir for the community,” Samuel said.

Managing battery and system performance

At the heart of storage systems are high-voltage battery modules – typically lithium-iron phosphate cells – capable of generating enormous amounts of heat if charged or discharged too quickly. These modules can also have shortened lifetimes if completely depleted too often.

Monitoring temperature and charge in these batteries requires extremely precise semiconductors, such as the BQ79616 industrial battery monitor, Samuel said. That’s because even tiny fluctuations in temperature and voltage can signal that a battery may need attention.

“You have to get to millivolt accuracy to know how much charge is left in a battery,” he said.

Our company’s extensive experience in ultra-precise battery monitors is proving essential in helping the ESS industry produce systems that can supply the grid with vital battery-management data. The results can have a big impact on the cost-effectiveness of a grid ESS, Samuel said.

“If you can only measure the charge in a 10-MWh ESS with 5% accuracy, then you can’t safely use more than 9.5 MWh,” he said. “Our battery monitors can get the accuracy measurement to 1%, which enables you to use 9.9 MWh.”

In addition to accurate battery monitoring, grid-scale energy storage systems such as the ones integrated with solar panel farms require efficient high-voltage power conversion that help reduce power losses when transferring power to and from the grid. These systems also rely on sensing and isolation technologies that help maintain system safety and stability, which is critical for managing electricity flow as high as 1500 V.

Impacting the future

For the foreseeable future, innovation in battery ESS looks to be the key to transform and protect the grid from the variabilities coming from solar and wind energy, as well as EV charging.

“It’s really exciting to contribute to strengthening the grid with innovations in energy storage,” Samuel said. “We can already do a lot today, and we’ll be able to do a lot more as we build out tomorrow’s smart grid.”

The post The battery-management technology that will strengthen our grid appeared first on ELE Times.

ROHM has developed the RLD8BQAB3 high-output laser diode for use in ADAS (Advanced Driver Assistance Systems) equipped with LiDAR for distance measurement and spatial recognition. In addition to automotive applications, the firm is also initially supplying samples targeting consumer applications (drones, robot vacuums, golf rangefinders) and industrial applications (AGVs, service robots, and 3D monitoring systems such as sensors for human/object detection)...

Temperature sensors are critical components in a variety of industries, from manufacturing and automotive to healthcare and environmental monitoring. Among the most common temperature-sensing devices are Resistance Temperature Detectors (RTDs), thermocouples, and thermistors. Each of these sensors has unique characteristics, advantages, and limitations, making them suitable for different applications. This article provides a detailed comparison to help you choose the right sensor for your needs.

1. Resistance Temperature Detectors (RTDs)

RTDs measure temperature by correlating the resistance of a material (usually platinum) to temperature. Platinum is preferred because of its stability and linear resistance-temperature relationship.

Key Features of RTDs:

• Accuracy: RTDs deliver exceptional precision, typically within ±0.1°C.
• Stability: They provide outstanding consistency and reliable performance over long durations.
• Temperature Range: Commonly operate effectively between -200°C and 600°C.
• Linearity: RTDs exhibit a near-linear relationship between resistance and temperature, simplifying data interpretation.

Advantages:

• Highly precise and reliable.
• Extended operational life with negligible performance degradation over time.
• Suitable for industrial and laboratory settings.

Limitations:

• Expensive compared to thermocouples and thermistors.
• Fragile and sensitive to physical shocks and vibrations.
• Requires external circuitry for resistance measurement.

2. Thermocouples

 

Thermocouples generate an electrical voltage that reflects the temperature gradient between two dissimilar metal junctions and a reference point. The voltage generated is interpreted to identify the corresponding temperature.

Key Features of Thermocouples:

• Versatility: Available in various types (e.g., Type J, K, T, E) to suit specific applications.
• Temperature Range: Capable of measuring temperatures from -200°C to over 2000°C, depending on the type.
• Durability: Resistant to mechanical stress and high temperatures.

Advantages:

• Wide temperature range.
• Cost-effective, especially for high-temperature applications.
• Their rapid response is attributed to a low thermal mass, enabling quick detection of temperature changes.

Limitations:

• Less accurate than RTDs, with typical errors of ±2°C to ±5°C.
• Requires regular calibration for precise measurements.
• Voltage signals are small and can be affected by electrical noise.

3. Thermistors

Thermistors are temperature-sensitive resistors made from ceramic or polymer materials. Their resistance decreases (Negative Temperature Coefficient, NTC) or increases (Positive Temperature Coefficient, PTC) significantly with temperature changes.

Key Features of Thermistors:

• Sensitivity: Extremely sensitive to small temperature changes.
• Temperature Range: Typically operate within -50°C to 150°C.
• Size: Compact and easy to integrate into electronic systems.

Advantages:

• High sensitivity enables precise detection of small temperature changes.
• Low cost and compact design.
• Quick response time.

Limitations:

• Limited temperature range.
• Non-linear response, requiring complex calibration.
• Thermistors tend to have lower durability in extreme or harsh environments when compared to RTDs and thermocouples.

Comparison Table

Feature	RTD	Thermistor	Thermocouple
Accuracy	High (±0.1°C)	High in a limited range	Moderate (±2°C to ±5°C)
Temperature Range	-200°C to 600°C	-50°C to 150°C	-200°C to 2000°C
Durability	Fragile	Moderate	Highly durable
Cost	Expensive	Economical	Affordable to mid-range
Response Time	Intermediate	Quick	Rapid
Linearity	Near-linear	Non-linear	Non-linear

 

Choosing the Right Sensor

Selecting a temperature sensor hinges on its intended use:

• RTDs: Preferred for applications needing high precision and consistent performance, such as in labs, industrial setups, and HVAC systems.
• Thermocouples: Well-suited for high-temperature or challenging environments, including metal forging, kilns, and aviation.
• Thermistors: Ideal for compact, cost-sensitive applications like household devices, medical instruments, and consumer gadgets.

Conclusion

RTDs, thermocouples, and thermistors are essential tools for temperature measurement, each with distinct strengths and weaknesses. Understanding their characteristics and applications ensures optimal performance and cost-efficiency in your projects. Whether you prioritize precision, range, or durability, selecting the appropriate sensor will significantly impact the success of your temperature-sensitive processes.

The post RTD vs Thermocouple vs Thermistor: Understanding Temperature Sensors appeared first on ELE Times.

STMicroelectronics, a global semiconductor leader serving customers across the spectrum of electronics applications, announced that it will release its fourth quarter and full year 2024 earnings before the opening of trading on the European Stock Exchanges on Thursday, January 30, 2025.

The press release will be available immediately after the release on the Company’s website at www.st.com.

STMicroelectronics will conduct a conference call with analysts, investors and reporters to discuss its fourth quarter and full year 2024 financial results and current business outlook on January 30, 2025, at 9:30 a.m. Central European Time (CET) / 3:30 a.m. U.S. Eastern Time (ET).

A live webcast (listen-only mode) of the conference call will be accessible at ST’s website https://investors.st.com and will be available for replay until February 14, 2025.

The post STMicroelectronics Announces Timing for Fourth Quarter and Full Year 2024 Earnings Release and Conference Call appeared first on ELE Times.

submitted by /u/DukequaX [link] [comments]

submitted by /u/flippont [link] [comments]

A bit on the software-defined vehicle (SDVs)

In-vehicle systems have massively grown in complexity with more installed speakers, microphones, cameras, displays, and compute burden to process the necessary information and provide the proper, often time-sensitive output. The unfortunate side effect of this complexity is the massive increase in ECUs and subsequent cabling to and from its allocated subsystem (e.g., engine, powertrain, braking, etc.). The lack of practicality with this approach has become apparent where more OEMs are shifting away from these domain-based architectures and subsequently traditional automotive buses such as local interconnect network (LIN), controlled area network (CAN) for ECU communications, FlexRay for x-by-wire systems, and media oriented transport (MOST) for audio and video systems. SDVs rethink underlying vehicle architecture so that cars are broken into zones that will directly service the vehicle subsystems that surround it locally, cutting down wiring, latency, and weight. Another major benefit of this are over-the-air (OTA) updates using Wi-Fi or cellular to update cloud-connected cars, however bringing ethernet to the automotive edge comes with its complexities.

ADI’s approach to zonal architectures

This year at CES, EDN spoke with Yasmine King, VP of automotive cabin experience at Analog Devices (ADI). The company is closely working with the underlying connectivity solutions that allow vehicle manufacturers to shift from domain architectures to zonal with ethernet-to-edge (E2B) bus, automotive audio bus (A2B), and gigabit multimedia serial link (GMSL) technology. “Our focus this year is to show how we are adding intelligence at the edge and bringing the capabilities from bridging the analog of the real world into the digital world. That’s the vision of where automotive wants to get to, they want to be able to create experiences for their customers, whether it’s the driving experience, whether it’s the back seat passenger experience. How do you help create these immersive and safe experiences that are personalized to each occupant in the vehicle? In order to do that, there has to be a fundamental change of what the architecture of the car looks like,” said King. “So in order to do this in a way that is sustainable, for mobility to remain green, remain long battery range, good fuel efficiency, you have to find a way of transporting that data efficiently, and the E2B bus is one of those connectivity solutions where it’s it allows for body control, ambient lighting.”

E2B: Remote control protocol solution 10BASE-T1S solution

Based on the OPEN alliance 10BASE-T1S physical layer (PHY), the E2B bus aims at removing the need for MCUs centralizing the software to the high performance compute (HPC) or central compute (Figure 1). “The E2B bus is the only remote control protocol solution available on the market today for the 10BASE-T1S so it’s a very strong position for us. We just released our first product in June of this past year, and we see this as a very fundamental way to help the industry transform to zonal architecture. We’re working with the OPEN alliance to be part of that remote control definition.” These transceivers will integrate low complexity ethernet (LCE) hardware for remote operation and, naturally, can be used on the same bus as any other 10BASE-T1S-compliant product

BMW has already adopted the E2B bus for their ambient lighting system, King mentioned that there has already been further adoption by other OEMs but they were not public yet. “The E2B bus is one of those connectivity solutions where it allows for body control, ambient lighting. Honestly, there’s about 50 or 60 different applications inside the vehicle.” She mentioned how E2B is often used for ambient lighting today but there are many other potential applications such as driver monitoring systems (DMSs) that might detect a sleeping driver via the in-vehicle biometric capabilities to then respond with a series of measures to wake them up, E2B allows OEMs to apply these measures with an OTA update. Without E2B, you’d have to not only update the DMS, but you’d have to update the multiple nodes that are controlling the ambient light. The owner might have to take it back into the shop to apply the updates, it just takes longer and is more of a hassle. With E2B, it’s a single OTA update that is an easy, quick download to add safety features so it’s more realistic to get that safer, more immersive driver experience.” The goal for ADI is to move all the software from all edge nodes to the central location for updates.

Figure 1: EDN editor, Aalyia Shaukat (left) and VP of automotive cabin experience, Yasmine King (right) in front of a suspension control demo with 4 edge nodes sensing the location of the weighted ball, sends the information back to the HPC to send commands back to control the motors. 

A2B: Audio system based on 100BASE-T1

Based upon the 100BASE-T1 standard, the A2B audio follows a similar concept of connecting edge nodes with a specialization in sound limiting the installation of weighty shielded analog cables going to and from the many speakers and microphones in vehicles today for modern functions such as active noise cancellation (ANC) and road noise cancellation (RNC). “We have  RNC algorithms that are connected through A2B, and it’s a very low latency, highly deterministic bus. It allows you to get the inputs from, say, the wheel base, where you’re listening for the noise, to the brain of the central compute very quickly.” King mentioned how audio systems require extremely low latencies for an enhanced user experience, “your ears are very susceptible to any small latency or distortion.” The technology has more maturity than the newer E2B bus and has therefore seen more adoption, “A2B is a technology that is utilized across most OEMs, the top 25 OEMs are all using it and we’ve shipped millions of ICs.” ADI is working on a second iteration of the A2B bus that multiplies the data rate of the previous generation, this is likely due to the maturation of the 1000BASE-T1 standard for automotive applications that is meant to reach 1 Gbps. When asked about the data rate King responded, “I’m not sure exactly what we are publicly stating yet but it will be a multiplier.” 

GMSL: Single-wire SerDes display solution

GMSL is the in-vehicle serializer/deserializer (SerDes) video solution that shaves off the significant wiring typically required with camera and subsequent sensor infrastructure (Figure 2). “As you’re moving towards autonomous driving and you want to replace a human with intelligence inside the vehicle, you need additional sensing capabilities along with radar, LiDAR, and cameras to be that perception sensing network. It’s all very high bandwidth and it needs a solution that can be transmitted in a low-cost, lightweight cable.” Following a similar theme as the E2B and A2B audio buses, using a single cable to manage a cluster display or an in-vehicle infotainment (IVI) human-to-machine interface (HMI) minimizes the potential weight issues that could damage range/fuel efficiency. King finished by mentioning one overlooked benefit of lowering the weight of vehicle harnessing “The other piece that often gets missed is it’s very heavy during manufacturing, when you move over 100 pounds within the manufacturing facilities you need different safety protocols. This adds expense and safety concerns for the individuals who have to pick up the harness where now you have to get a machine over to pick up the harness because it’s too heavy.”

Figure 2: GMSL demo aggregating feeds from six cameras into a deserializer board going into a single MIPI port on the Jetson HPC-platform.

Aalyia Shaukat, associate editor at EDN, has worked in the design publishing industry for six years. She holds a Bachelor’s degree in electrical engineering from Rochester Institute of Technology, and has published works in major EE journals as well as trade publications.

Related Content

• CES 2025: Moving toward software-defined vehicles
• CES 2025’s sensor design highlights

The post ADI’s efforts for a wirelessly upgraded software-defined vehicle appeared first on EDN.

If you’re not careful with this thing, it’ll probably lop off your fingers: https://imgur.com/a/XuLKBF1 submitted by /u/Asuntofantunatu [link] [comments]

Digital potentiometers (“Dpots”) are a diverse and useful category of digital/analog components with up to a 10-bit resolution, element resistance from 1k to 1M, and voltage capability up to and beyond ±15v. However, most are limited to 8 bits, monopolar (typically 0v to +5v) signal levels, and 5k to 100k resistances with loose tolerances of ±20 to 30%.

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

This design idea describes a simple and inexpensive Dpot-like alternative. It has limitations of its own (mainly being restricted to relatively low signal frequencies) but offers useful and occasionally superior performance in areas where actual Dpots tend to fall short. These include parameters like bipolar signal range, terrific differential nonlinearity, tight resistance accuracy, and programmable resolution. See Figure 1.

Figure 1 PWM drives opposing-phase CMOS switches and RC network to simulate a Dpot

RC ripple filtering limits frequency response to typically tens to hundreds of Hz.

Switch U1b connects wiper node W to node B when PWM = 1, and to A when PWM = 0. Letting the PWM duty factor, P = 0 to 1, and assuming no excessive loading of W:

Vw = P(Vb – Va) + Va

 Meanwhile, switch U1a connects W to node A when PWM = 1, and to B when PWM = 0, thus 180o out of phase with U1b. Due to AC coupling, this has no effect on pot DC output, but the phase inversion relative to U1b delivers active ripple attenuation as described in “Cancel PWM DAC ripple with analog subtraction.”

The minimum RC time-constant required to attenuate ripple to no more than 1 least significant bit (lsb) for any given N = number of PWM bits of resolution and Tpwm = PWM period is given by:

RC = Tpwm 2(N/2 – 2)  

For example:

for N = 8, Fpwm = 10 kHz

RC = 10 kHz-1*2(8/2 – 2)  = 100 µs*22 = 400 µs

The maximum acceptable value for R is dictated by the required Vw voltage accuracy under load. Minimum R is determined by:

1. Required resistance accuracy after factoring in the variability of U1b switch Ron: r which is 40 ±40Ω for the HC4053 powered as in Figure 1.
2. Required integral nonlinearity (INL) as affected by switch-to-switch Ron variation, which is just 5 Ω for the HC4053 as powered here.

 R = 1k to 10k would be a workable range of choices for N = 8-bit resolution. N is programmable.

The net result is the equivalent circuit shown in Figure 2. Note that, unlike a mechanical pot or Dpot, where output resistance varies dramatically with wiper setting, the PWMpot’s output resistance (R +r) is nominally constant and independent of setting.

Figure 2 The PWMpot’s equivalent circuit where r = switch Ron, P = PWM duty factor, and where the ripple filter capacitors are not shown.

 Funny footnote: While pondering a name for this idea, I initially thought “PWMpot” was too long and considered making it shorter and catchy-er by dropping the “WM.” But then, after reading the resulting acronym out loud, I decided it was maybe a little too catchy.

And put the “WM” back!

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

• Cancel PWM DAC ripple with analog subtraction
• A faster PWM-based DAC
• Parsing PWM (DAC) performance: Part 1—Mitigating errors
• PWM power DAC incorporates an LM317
• Cancel PWM DAC ripple with analog subtraction but no inverter
• Cancel PWM DAC ripple with analog subtraction—revisited

The post PWMpot approximates a Dpot appeared first on EDN.

At the Consumer Electronics Show (CES 2025) in Las Vegas (7–11 January), Aledia S.A of Echirolles, near Grenoble, France (a developer and manufacturer of 3D micro-LEDs for display applications based on its large-area gallium nitride nanowires-on-silicon platform) has announced the availability of its micro-LED technology for augmented reality applications and next-generation displays for vision applications...

A network switch is a hardware device that connects devices within a Local Area Network (LAN) to enable communication. It operates at the data link layer (Layer 2) or network layer (Layer 3) of the OSI model and uses MAC or IP addresses to forward data packets to the appropriate device. Unlike hubs, switches efficiently direct traffic to specific devices rather than broadcasting to all network devices.

Types of Network Switch

1. Unmanaged Switch:
   • Basic plug-and-play device with no configuration options.
   • Suitable for small or home networks.
2. Managed Switch:
   • Allows advanced configuration, monitoring, and control.
   • Used in enterprise networks for better security and performance management.
3. Smart Switch:
   • A middle ground between unmanaged and managed switches.
   • Provides limited management features for smaller networks.
4. PoE Switch (Power over Ethernet):
   • Delivers power to connected devices such as VoIP phones and IP cameras.
5. Layer 3 Switch:
   • Integrates routing functions with Layer 2 switching capabilities.
   • Ideal for larger, more complex networks.

How Does a Network Switch Work?

A network switch operates by analyzing incoming data packets, determining their destination addresses, and forwarding them to the correct port. It maintains a MAC address table that maps devices to specific ports, ensuring efficient communication.

Steps in operation:

1. Receives data packets.
2. Reads the packet’s destination MAC or IP address.
3. Matches the address with its internal table to find the correct port.
4. Forwards the packet only to the intended recipient device.

Network Switch Uses & Applications

• Home Networks: Connect devices like PCs, printers, and smart home systems.
• Enterprise Networks: Facilitate communication across servers, workstations, and other IT infrastructure.
• Data Centers: Support high-speed communication and load balancing.
• Industrial Applications: Manage devices in IoT and automation systems.
• Surveillance Systems: Power and connect IP cameras via PoE switches.

How to Use a Network Switch

1. Select the Right Switch: Choose based on your network size and requirements (e.g., unmanaged for simple networks, managed for complex ones).
2. C Connect Devices: Insert Ethernet cables from your devices into the available ports on the switch.
3. Connect to a Router: Link the switch to a router for internet access.
4. Power On the Switch: If using PoE, ensure the switch supports the connected devices.
5. Configure (if applicable): For managed switches, use the web interface or CLI to set up VLANs, QoS, or security settings.

Network Switch Advantages

• Improved Network Efficiency: Directs traffic only to the intended recipient device.
• Scalability: Allows multiple devices to connect and communicate.
• Enhanced Performance: Supports higher data transfer rates and reduces network congestion.
• Security Features: Managed switches offer advanced security controls.
• Flexibility: PoE switches provide power to connected devices, removing the requirement for individual power sources.

 

The post Network Switch Meaning, Types, Working, Benefits & Applications appeared first on ELE Times.

An eSIM (embedded SIM) is an integrated SIM solution embedded within a device, removing the necessity for a physical SIM card. Integrated into a device’s hardware, it enables users to activate a mobile network plan without the need for a physical SIM card. This technology simplifies connectivity and is gaining popularity in smartphones, wearables, IoT devices, and automotive applications.

How Does eSIM Work?
An eSIM functions through a reprogrammable SIM chip that is built into the device’s hardware. In contrast to traditional SIM cards that require physical replacement, eSIMs can be activated or reconfigured using software. Mobile network operators (MNOs) provide QR codes or activation profiles that users scan or download to enable network connectivity.

The process typically involves the following steps:
1. Provisioning: The user receives a QR code or activation data from the MNO.
2. Activation: The eSIM-capable device connects to the MNO’s server to download and install the profile.
3. Switching Networks: Users can store multiple profiles and switch between them as needed.

eSIM Architecture
The architecture of an eSIM integrates hardware and software components to ensure seamless connectivity:
1. eUICC (Embedded Universal Integrated Circuit Card): This is the hardware component that houses the eSIM profile.
2. Profile Management: eSIM profiles are managed remotely by MNOs using Over-the-Air (OTA) technology.
3. Security Framework: Ensures secure provisioning, activation, and data transmission.
4. Interoperability Standards: Governed by GSMA specifications to ensure compatibility across devices and networks.

Types of eSIM
1. Consumer eSIM: Designed for smartphones, tablets, and wearables to provide seamless personal connectivity.
2. M2M (Machine-to-Machine) eSIM: Designed for IoT devices to enable seamless global connectivity.
3. Automotive eSIM: Implemented in connected cars for telematics, navigation, and emergency services.

eSIM Uses & Applications

1. Smartphones and Wearables:
– Enables dual SIM functionality.
– SMakes it easy to switch between carriers without needing to replace SIM cards.

2. IoT Devices:
– Powers smart meters, trackers, and sensors with global connectivity.

3. Automotive:
– Supports connected car applications like real-time navigation, diagnostics, and emergency calls.

4. Travel:
– Allows travelers to activate local plans without buying physical SIMs.

5. Enterprise:
– Facilitates centralized management of employee devices.

How to Use eSIM

1. Verify Device Compatibility: Confirm that the device is equipped with eSIM support.
2. Obtain an eSIM Plan: Contact an MNO to get an eSIM-enabled plan.
3. Activate the eSIM:
– Use the QR code supplied by the network operator for activation.
– Adhere to the displayed prompts to download and set up the eSIM profile.
4. Manage Profiles: Use the device settings to switch between profiles or add new ones.

Advantages of eSIM

1. Convenience: Removes the dependency on physical SIM cards for connectivity.
2. Flexibility: Supports multiple profiles, enabling seamless switching between carriers.
3. Compact Design: Saves space in devices, allowing for sleeker designs or additional features.
4. Remote Provisioning: Simplifies activation and profile management.
5. Eco-Friendly: Reduces plastic waste from physical SIM cards.

Disadvantages of eSIM
1. Limited Compatibility: eSIM technology is not universally supported across all devices.
2. Dependency on MNOs: Activation relies on operator support.
3. Security Concerns: Potential vulnerability during OTA provisioning.
4. Complexity in Migration: Switching devices requires transferring eSIM profiles, which can be less straightforward than swapping physical SIMs.

What is an eSIM Card?
An eSIM card is a built-in chip integrated into the device’s hardware, functioning as a replacement for conventional SIM cards. It operates electronically, allowing devices to connect to networks without physical card insertion.eSIM Module for IoT
In IoT, eSIM modules are integral for providing reliable, scalable, and global connectivity. They:
– Enable remote management of IoT devices.
– Streamline logistics by removing the necessity for region-specific SIM cards.
– Provide a robust solution for devices operating in diverse environments.

Conclusion

eSIM technology represents a significant step forward in connectivity, offering unmatched flexibility and convenience. From smartphones to IoT devices, its applications are broad and transformative. While it has limitations, advancements in compatibility and security are likely to drive its widespread adoption in the coming years.

The post eSIM Meaning, Types, Working, Card, Architecture & Uses appeared first on ELE Times.

Artificial intelligence (AI) is expanding rapidly to the edge. This generalization conceals many more specific advances—many kinds of applications, with different processing and memory requirements, moving to different kinds of platforms. One of the most exciting instances, happening soonest and with the most impact on users, is the appearance of TinyML inference models embedded at the extreme edge—in smart sensors and small consumer devices.

Figure 1 The TinyML inference models are being embedded at the extreme edge in smart sensors and small consumer devices. Source: PIMIC

This innovation is enabling valuable functions such as keyword spotting (detecting spoken keywords) or performing environmental-noise cancellation (ENC) with a single microphone. Users treasure the lower latency, reduced energy consumption, and improved privacy.

Local execution of TinyML models depends on the convergence of two advances. The first is the TinyML model itself. While most of the world’s attention is focused on enormous—and still growing—large language models (LLMs), some researchers are developing really small neural-network models built around hundreds of thousands of parameters instead of millions or billions. These TinyML models are proving very capable on inference tasks with predefined inputs and a modest number of inference outputs.

The second advance is in highly efficient embedded architectures for executing these tiny models. Instead of a server board or a PC, think of a die small enough to go inside an earbud and efficient enough to not harm battery life.

Several approaches

There are many important tasks involved in neural-network inference, but the computing workload is dominated by matrix multiplication operations. The key to implementing inference at the extreme edge is to perform these multiplications with as little time, power, and silicon area as possible. The key to launching a whole successful product line at the edge is to choose an approach that scales smoothly, in small increments, across the whole range of applications you wish to cover.

It is the nature of the technology that models get larger over time.

System designers are taking different approaches to this problem. For the tiniest of TinyML models in applications that are not particularly sensitive to latency, a simple microcontroller core will do the job. But even for small models, MCUs with their constant fetching, loading, and storing are not an energy-efficient approach. And scaling to larger models may be difficult or impossible.

For these reasons many choose DSP cores to do the processing. DSPs typically have powerful vector-processing subsystems that can perform hundreds of low-precision multiply-accumulate operations per cycle. They employ automated load/store and direct memory access (DMA) operations cleverly to keep the vector processors fed. And often DSP cores come in scalable families, so designers can add throughput by adding vector processor units within the same architecture.

But this scaling is coarse-grained, and at some point, it becomes necessary to add a whole DSP core or more to the design, and to reorganize the system as a multicore approach. And, not unlike the MCU, the DSP consumes a great deal of energy in shuffling data between instruction memory and instruction cache and instruction unit, and between data memory and data cache and vector registers.

For even larger models and more latency-sensitive applications, designers can turn to dedicated AI accelerators. These devices, generally either based on GPU-like SIMD processor arrays or on dataflow engines, provide massive parallelism for the matrix operations. They are gaining traction in data centers, but their large size, their focus on performance over power, and their difficulty in scaling down significantly make them less relevant for the TinyML world at the extreme edge.

Another alternative

There is another architecture that has been used with great success to accelerate matrix operations: processing-in-memory (PiM). In this approach, processing elements, rather than being clustered in a vector processor or pipelined in a dataflow engine, are strategically dispersed at intervals throughout the data memory. This has important benefits.

First, since processing units are located throughout the memory, processing is inherently highly parallel. And the degree of parallel execution scales smoothly: the larger the data memory, the more processing elements it will contain. The architecture needs not change at all.

In AI processing, 90–95% of the time and energy is consumed by matrix multiplication, as each parameter within a layer is computed with those in subsequent layers. PiM addresses this inefficiency by eliminating the constant data movement between memory and processors.

By storing AI model weights directly within memory elements and performing matrix multiplication inside the memory itself as input data arrives, PiM significantly reduces data transfer overhead. This approach not only enhances energy efficiency but also improves processing speed, delivering lower latency for AI computations.

To fully leverage the benefits of PiM, a carefully designed neural network processor is crucial. This processor must be optimized to seamlessly interface with PiM memory, unlocking its full performance potential and maximizing the advantages of this innovative technology.

Design case study

The theoretical advantages of PiM are well established for TinyML systems at the network edge. Take the case of Listen VL130, a voice-activated wake word inference chip,which is also PIMIC’s first product. Fabricated on TSMC’s standard 22-nm CMOS process, the chip’s always-on voice-detection circuitry consumes 20 µA.

This circuit triggers a PiM-based wake word-inference engine that consumes only 30 µA when active. In operation, that comes out to a 17-times reduction in power compared to an equivalent DSP implementation. And the chip is tiny, easily fitting inside a microphone package.

Figure 2 Listen VL130, connected to external MCU in the above diagram, is an ultra-low-power keyword-spotting AI chip designed for edge devices. Source: PIMIC

PIMIC’s second chip, Clarity NC100, takes on a more ambitious TinyML model: single-microphone ENC. Consuming less than 200 µA, which is up to 30 times more efficient than a DSP approach, it’s also small enough for in-microphone mounting. It is scheduled for engineering samples in January 2025.

Both chips depend for their efficiency upon a TinyML model fitting entirely within an SRAM-based PiM array. But this is not the only way to exploit PiM architectures for AI, nor is it anywhere near the limits of the technology.

LLMs at the far edge?

One of today’s undeclared grand challenges is to bring generative AI—small language models (SLMs) and even some LLMs—to edge computing. And that’s not just to a powerful PC with AI extensions, but to actual edge devices. The benefit to applications would be substantial: generative AI apps would have greater mobility while being impervious to loss of connectivity. They could have lower, more predictable latency; and they would have complete privacy. But compared to TinyML, this is a different order of challenge.

To produce meaningful intelligence, LLMs require training on billions of parameters. At the same time, the demand for AI inference compute is set to surge, driven by the substantial computational needs of agentic AI and advanced text-to-video generation models like Sora and Veo 2. So, achieving significant advancements in performance, power efficiency, and silicon area (PPA) will necessitate breakthroughs in overcoming the memory wall—the primary obstacle to delivering low-latency, high-throughput solutions.

Figure 3 Here is a view of the layout of Listen VL130 chip, which is capable of processing 32 wake words and keywords while operating in the tens of microwatts, delivering energy efficiency without compromising performance. Source: PIMIC

At this technology crossroads, PiM technology is still important, but to a lesser degree. With these vastly larger matrices, the PiM array acts more like a cache, accelerating matrix multiplication piecewise. But much of the heavy lifting is done outside the PiM array, in a massively parallel dataflow architecture. And there is a further issue that must be resolved.

At the edge, in addition to facilitate model execution, it’s of primary importance to resolve the bandwidth and energy issues that come with scaling to massive memory sizes. Meeting all these challenges can improve an edge chip’s power-performance-area efficiency by more than 15 times.

PIMIC’s studies indicate that models with hundreds of millions to tens of billions of parameters can in fact be executed on edge devices. It will require 5-nm or 3-nm process technology, PiM structures, and most of all a deep understanding of how data moves in generative-AI models and how it interacts with memory.

PiM is indeed a silver bullet for TinyML at the extreme edge. But it’s just one tool, along with dataflow expertise and deep understanding of model dynamics, in reaching the point where we can in fact execute SLMs and some LLMs effectively at the far edge.

Subi Krishnamuthy is the founder and CEO of PIMIC, an AI semiconductor company developing processing-in-memory (PiM) technology for ultra-low-power AI solutions.

 

Related Content

• Getting a Grasp on AI at the Edge
• Tiny machine learning brings AI to IoT devices
• Why MCU suppliers are teaming up with TinyML platforms
• Open-Source Development Comes to Edge AI/ML Applications
• Edge AI: The Future of Artificial Intelligence in embedded systems

The post AI at the edge: It’s just getting started appeared first on EDN.

Aehr Test Systems of Fremont, CA, USA has received an initial production order from a top-tier automotive semiconductor supplier for a FOX-XP wafer-level test and burn-in system with fully integrated FOX WaferPak Aligner for production test of its gallium nitride (GaN) power semiconductor devices. The system was scheduled to ship immediately...

Keysight Technologies announces the expansion of its Novus portfolio with the Novus mini automotive, a quiet small form-factor pluggable (SFP) network test platform that addresses the needs of automotive network engineers as they deploy software defined vehicles (SDV). Keysight is expanding the capability of the Novus platform by offering a next generation vehicle interface that includes 10BASE-T1S, and multi-gigabyte BASE-T1 support for 100 megabytes per second, 2.5 gigabits per second (Gbit/s), 5Gbit/s, and 10Gbit/s. Keysight’s SFP architecture provides a flexible platform to mix and match speeds for each port with modules plugging into existing cards rather than requiring a separate card, as many current test solutions necessitate.

As vehicles move to zonal architectures, connected devices are a critical operational component.  As a result, any system failures caused by connectivity and network issues can impact safety and potentially create life-threatening situations. To mitigate this risk, engineers must thoroughly test the conformance and performance of every system element before deploying them.

Key benefits of the Novus mini automotive platform include:

• Streamlines testing – The combined solution offers both traffic generation and protocol testing on one platform. With both functions on a single platform, engineers can optimize the testing process, save time, and simplify workflows without requiring multiple tools. It also accelerates troubleshooting and facilitates efficient remediation of issues.
• Helps lower costs and simplify wiring – Supports native automotive interfaces BASE-T1 and BASE-T1S that help lower costs and simplify wiring for automotive manufacturers, reducing the amount of required cabling and connectors. BASE-T1 and BASE-T1S offer a scalable and flexible single-pair Ethernet solution that can adapt to different vehicle models and configurations. These interfaces support higher data rates compared to traditional automotive communication protocols for faster, more efficient data transmission as vehicles become more connected.
• Compact, quiet, and affordable – Features the smallest footprint in the industry with outstanding cost per port, and ultra-quiet, fan-less operation.
• Validates layers 2-7 in complex automotive networks– Provides comprehensive performance and conformance testing that covers everything from data link and network protocols to transport, session, presentation, and application layers. Validating the interoperability of disparate components across layers is necessary in complex automotive networks where multiple systems must seamlessly work together.

• Protects networks from unauthorized access – Supports full line rate and automated conformance testing for TSN 802.1AS 2011/2020, 802.1Qbv, 802.1Qav, 802.1CB, and 802.1Qci. The platform tests critical timing standards for automotive networking, as precise timing and synchronization are crucial for the reliable and safe operation of ADAS and autonomous vehicle technologies. Standards like 802.1Qci help protect networks from unauthorized access and faulty or unsecure devices.

Ram Periakaruppan, Vice President and General Manager, Network Test & Security Solutions, Keysight, said: “The Novus mini automotive provides real-world validation and automated conformance testing for the next generation of software defined vehicles. Our customers must trust that their products consistently meet quality standards and comply with regulatory requirements to avoid costly fines and penalties. The Novus mini allows us to deliver this confident assurance with a compact, integrated network test solution that can keep pace with constant innovation.”

Keysight will demonstrate its portfolio of test solutions for automotive networks, including the Novus mini automotive, at the Consumer Technology Show (CES), January 7-10th in Las Vegas, NV, West Hall, booth 4664 (Inside the Intrepid Controls booth).

The post Keysight Expands Novus Portfolio with Compact Automotive Software Defined Vehicle Test Solution appeared first on ELE Times.

Soft soldering is a popular technique in metal joining, known for its simplicity and versatility. It involves the use of a low-melting-point alloy to bond two or more metal surfaces. The process is widely used in electronics, plumbing, and crafting due to its ease of application and the reliability of the joints it produces.

What is Soft Soldering?

Soft soldering refers to the process of joining metals using a filler material, known as solder, that melts and flows at temperatures below 450°C (842°F). Unlike brazing or welding, the base metals are not melted during this process. The bond is achieved by the solder adhering to the surface of the base metals, which must be clean and properly prepared to ensure a strong joint.

The solder typically consists of tin-lead alloys, although lead-free alternatives are now common due to health and environmental concerns. Flux is often used alongside solder to remove oxides from the metal surfaces, promoting better adhesion and preventing oxidation during heating.

How Soft Soldering Works

Soft soldering is a straightforward process that follows these basic steps:

1. Preparation:
   • Clean the surfaces to be joined by removing dirt, grease, and oxidation. This can be done using sandpaper, a wire brush, or chemical cleaners.
   • Apply flux to the cleaned surfaces to prevent oxidation during heating and enhance solder flow.
2. Heating:
   • Utilize a soldering iron, soldering gun, or any appropriate heat source to warm the joint. Make sure the temperature is adequate to liquefy the solder while keeping the base metals intact.
3. Application of Solder:
   • After heating the joint, introduce the solder to the targeted area. The solder will melt and flow into the joint by capillary action, creating a strong bond upon cooling.
4. Cooling:
   • Let the joint cool down gradually without being disturbed. This ensures the integrity of the bond and prevents the formation of weak spots.

Soft Soldering Process 1. Tools and Materials

The essential tools and materials for soft soldering include:

• Soldering iron or gun
• Solder (tin-lead or lead-free)
• Flux
• Cleaning tools (e.g., sandpaper, wire brush)
• Heat-resistant work surface

2. Detailed Steps

1. Surface Preparation: Clean the metal surfaces thoroughly. Apply flux to prevent oxidation and enhance solder adherence.
2. Preheating: Warm the area to ensure uniform heating and improve solder flow.
3. Solder Application: Melt the solder onto the heated joint, ensuring it flows evenly.
4. Inspection: Examine the joint for uniformity and proper adhesion.
5. Cleanup: Remove excess flux residue to prevent corrosion.

Uses and Applications

Soft soldering is widely employed in various industries and applications, including:

1. Electronics:
   • Circuit board assembly
   • Wire connections
   • Repair of electrical components
2. Plumbing:
   • Joining copper pipes
   • Creating watertight seals in plumbing joints for water supply systems
3. Jewellery Making:
   • Crafting and repairing delicate metal items
4. Arts and Crafts:
   • Creating stained glass
   • Assembling small metal models
5. Automotive Repairs:
   • Fixing radiators and other small components

Advantages of Soft Soldering

1. Ease of Use: The process is simple and does not require extensive training.
2. Low Temperature: Operates at lower temperatures, reducing the risk of damaging components.
3. Versatility: Capable of accommodating diverse materials and a variety of applications..
4. Cost-Effective: Requires minimal equipment and materials.
5. Repairability: Joints can be easily reworked or repaired.

Disadvantages of Soft Soldering

1. Weak Joint Strength: The bond is not as strong as those produced by welding or brazing.
2. Temperature Limitations: Joints may fail under high temperatures.
3. Toxicity: Lead-based solders pose health risks, necessitating the use of proper ventilation and safety measures.
4. Corrosion Risk: Residual flux can lead to corrosion if not cleaned properly.
5. Limited Material Compatibility: Not suitable for all types of metals, especially those with high melting points.

Conclusion

Soft soldering remains a valuable technique for joining metals in numerous applications, particularly where ease of use and low-temperature operation are essential. Its advantages make it ideal for delicate tasks in electronics, plumbing, and crafting, while its limitations must be considered when high strength or temperature resistance is required. With advancements in soldering materials and techniques, soft soldering continues to be a reliable and accessible method for metal joining.

The post Soft Soldering Definition, Process, Working, Uses & Advantages appeared first on ELE Times.

Neural implants contain integrated circuits (ICs) — commonly called chips — built on silicon. These implants need to be small and flexible to mimic circumstances inside the human body.

Binary to 7 segment display I've spent the past couple of days making a logic simulation inspired by Sebastian Lague's video series. It's missing quite a few features I wanted to initially add, but I wanted to share my progress. This is the link to the github repository: https://github.com/flippont/simple-program-editor And the live demo: https://flippont.github.io/simple-program-editor/ The controls are in the README file. submitted by /u/flippont [link] [comments]