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Three recent research efforts highlight how the quantum field is moving from laboratory experiments to scalable, commercial-ready technologies.

It goes without saying that battery-powered devices are sensitive to power draw, especially during periods of inactivity. One such use case is in sensor nodes or portable sensors—these devices passively monitor a specific condition. When the threshold is exceeded, they trigger an alarm or log the event for further analysis. Since most devices incorporate some form of microcontroller (MCU), selecting an MCU with intelligent analog peripherals can reduce the Bill of Materials (BOM) by performing the same functions of a discrete device while potentially saving power by disabling the analog functionality when not needed.

To demonstrate these features, we built two demos on the PIC16F17576 microcontroller family. One demo aims to use as little power as possible while detecting temperature changes, while the other utilizes the embedded op-amps to dynamically adjust the gain based on the input signal.

Power consumption

Let’s start at the top—power consumption. No matter how you slice it, all roads will lead to the same basic tenets:

• Keep VDD as low as possible
• Minimize oscillator frequency
• Turn off all unused peripherals and external circuits, when possible, and as much as possible
• Avoid floating nodes on digital I/O

Beyond this advice, it becomes a lot more application-specific. For instance, most op-amps and ADCs don’t have an OFF switch. This is where intelligent analog peripherals fit into designs.

The “intelligent” part of their name is derived from the fact that they can be controlled in software. While most analog peripherals would not be considered power hungry, when optimizing battery life, every little bit of current matters, and generally, there is a higher quiescent current draw that the discrete device would have due to process limitations.

However, there are special low-power peripherals that allow for ultra-low power operation, even when enabled all the time. For instance, the Low Power Voltage Reference (VREFLP) and Low Power Analog Comparator (CMPLP) in the PIC16F17576 family of MCUs draw minimal power but can trigger interrupts to wake the CPU if action is needed.

For devices without these lower power peripherals, another peripheral available in PIC MCUs is the Analog Peripheral Manager (APM). The APM is a specialized counter that can toggle power ON/OFF to the analog peripherals while allowing the CPU to remain continuously in sleep.

If an event occurs, requiring intervention from the CPU, the peripherals can generate an interrupt to wake the device. This avoids having to perform the following sequence: wake the CPU, power on the peripherals, check the results, perform an action, shut down the peripherals, and return to deep sleep.

Low-power demo

The objective of the low-power demo is to demonstrate the new CMPLP and VREFLP as a temperature alarm. This application could be used for cold asset tracking to log when an event over the expected temperature occurs. For the demo implementation, we designed a circuit to detect when a person touches the thermistor(s), causing a rise in temperature.

Figure 1 A finished low power demo prototype that will detects the temperature rise that occurs when a person touches the thermistor(s).

Theory of operation

This circuit is composed of two PIC16F17576 MCUs; one device acts like the device under test (DUT) while the other handles power measurement and display.

Power measurement and display

To measure the minuscule amount of current pulled by the MCU DUT, it was important to design a circuit that could perform high-side current sensing while also being capable of maintaining the power supply at 1.8 V, which is the lowest recommended operating voltage for this device family. For reference, the minimum operating voltage is 1.62 V, which provides a 10% margin on the power supply before the device is out of specified operating conditions.

To measure the quiescent current of the MCU and low-power analog peripherals, a precision 1:1 current mirror IC was used to supply current to the DUT (Figure 2). This IC has a settable compliance output limit, but the tolerancing and ranging on the internal reference was not acceptable for our purposes, so we overdrive the integrated circuit with an external 1.8-V reference (MCP1501-18E) to avoid having to calibrate each unit individually.

Figure 2 The high-side current circuit to measure the minuscule amount of current pulled by the MCU DUT, and 1.8-V DUT power supply.

This ensures the power rail for the DUT is as close as possible to 1.8 V. Guard rings and planes are placed on the PCB to minimize the leakage current of this rail as much as possible. The 1:1 current output goes through a sense resistor, and then a differential measurement of the voltage at the resistor is performed with a 24-bit delta-sigma ADC (MCP3564R) with an external 2.048-V voltage reference (MCP1501-20E). This is shown in Figure 3. The resulting measurement is then displayed on the OLED screen attached to the board.

Figure 3 The ADC implementation where the differential measurement of the voltage at the resistor is performed with a 24-bit delta-sigma ADC with an external 2.048-V voltage reference.

A (good) problem we discovered late in the process was that the current measurement in this configuration is so stable, it looks hard-coded on the display. Thankfully, this can be easily disproved by gently touching the DUT’s decoupling capacitors with a finger or other slightly conductive object and observing the change in measured current.

DUT

The DUT device performs a simple but crucial role in detecting temperature changes with as little power consumption as possible. For this, CMPLP and VREFLP are used together with the Peripheral Pin Select (PPS) system to output the state of the CMPLP without waking the CPU.

In an actual application, CMPLP’s output edge (LOW HIGH) would be used to wake the CPU to perform some action like logging a temperature event or sounding an alarm.

Using the high-side current measurement circuit designed, we found the current of the microcontroller in this state is ~2.2 to 2.4 μA, but there is room for a tiny bit of extra power savings.

VREFLP is comprised of two separate subsystems: a low-power 1-V reference and a low-power DAC. This application uses the slightly more power-hungry low-power DAC instead of the fixed 1-V reference because the temperature change from physical contact is very small, and the system must recalibrate the threshold on startup to account for environmental variance. In an application where a few degrees of tolerance are acceptable, using the 1-V reference would save a few fractions of a microamp.

Notably, this demo does not use the APM because the APM requires an oscillator to remain active, consuming a little bit more power (~2.8 μA) than simply leaving these ultra-low power modules on. In a situation where multiple analog peripherals are being used, such as the integrated op-amps, ADC, etc., the APM would provide significant savings in power.

Dynamic gain

Another feature of intelligent analog peripherals is the ability to adjust on the fly. In some cases, a signal may have a large dynamic range that is tricky to measure without clipping.

Clipping a signal is usually considered undesirable, as waveform information about the signal is lost. A simple example of this is a microphone: whispering requires a high gain while shouting requires a low gain. With a fixed gain, designers pick the worst (reasonable) conditions to avoid signal clipping, but this, in turn, reduces the signal resolution.

A way around this problem is to use embedded op-amps. These op-amps aren’t going to outmatch the high-end op-amps, but they are often comparable to general-purpose ones.

And, in many cases, the integrated op-amps contain built-in resistor networks that allow the op-amp(s) to adjust the circuit gain as needed. This requires no extra components or specialized circuitry as it’s already integrated into the die.

Dynamic gain demo

One of the main use cases for the integrated op-amps inside MCUs is to dynamically switch gains depending on how strong the signal is. This is often performed to avoid clipping the signal when the signal strength is high.

This application creates a simple demonstration of this use case by amplifying the output of a pressure sensor and displaying it visually on an LED bar graph.

Figure 4 A dynamic gain demo that amplifies the output of a pressure sensor and displays it visually on an LED bar graph.

Theory of operation Pressure sensor

The pressure sensor in this application changes resistance depending on the amount of pressure applied. This resistor is used as part of a resistor divider network to generate an output signal from 0 to 2 V. Since both the discrete op-amp and the integrated op-amp have high-input impedances, the two circuits can share the same signal without loading down the network.

Dynamic gain circuit

The PIC16F17576 MCU has four op-amps, with two of them containing integrated resistor ladders. These ladders have eight steps, plus an additional option for unity gain (1x), for a total of nine options. Alternatively, resistors or other components can be connected to the I/O pins to assign an arbitrary gain or function, if desired.

In this demo, the MCU’s op-amp is switched between a gain of 2x (LOW) and 4x (HIGH) at runtime depending on the measured signal.

In most applications, when the signal strength is low, the gain would be HIGH. However, it is worth noting that in this demo, the inverse is true. This is purely for visual reasons; otherwise, the clipping condition would have more lights ON and thus appear “better” than the dynamic gain version at a glance. As the gain of the embedded op-amps is set up in software, it was easily reconfigured to match the desired behavior.

Measurement and display

The PIC16F17576 MCU also performs the measurement of both op-amp outputs to display on the LED bar graph. The internal Fixed Voltage Reference (FVR) is used to generate a stable 4.096 V from the +5-V (USB) supply for conversions. MCP23017 I2C I/O expanders are used to drive the LEDs of the display. 

Putting it all together

Adjusting the circuit gain without any external circuitry greatly simplifies designs where there are large signal ranges. These peripherals, of course, will not replace high-performance op-amps, ADCs, DACs, or voltage references, but embedded analog peripherals are a good way to handle signals that require some conditioning but aren’t particularly sensitive. This, coupled with low power functionality, makes them a useful tool to reduce circuit complexity, time to market, and ultimately the BOM in your design.

Robert Perkel is an application engineer for Microchip Technology. In this role, he develops technical content such as App Notes, contributed articles, and videos. He is also responsible for analyzing use cases of peripherals and the development of code examples and demonstrations. Perkel is a graduate of Virginia Tech, where he earned a Bachelor of Science degree in Computer Engineering.

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Toshiba Electronic Devices & Storage Corp of Kawasaki, Japan has launched three 650V silicon carbide (SiC) MOSFETs equipped with its latest third-generation SiC MOSFET chips and housed in general-purpose surface-mount TOLL packages...

SweGaN AB of Linköping, Sweden — a manufacturer of custom gallium nitride on silicon carbide (GaN-on-SiC) epitaxial wafers, based on proprietary growth technology — is coordinator of the project ‘GaN6G+: Unlocking Performance and Efficiency in Future 6G Power Amplifiers’, partnered by communications network provider Ericsson, defense and security company Saab and Chalmers University of Technology in Gothenburg, Sweden. Funded by Swedish innovation agency Vinnova and lasting from September 2025 to August 2027, the project aims to revolutionize GaN-based power amplifier (PA) technology for next-generation 6G networks...

📰 Газета "Київський політехнік" № 29-30 за 2025 (.pdf) Інформація КП чт, 08/28/2025 - 15:57

An MCU facilitating real-time control in motor control and power conversion applications incorporates post-quantum cryptography (PQC) requirements for firmware protection outlined in the Commercial National Security Algorithm (CNSA) Suite 2.0. These MCUs also support Platform Security Architecture (PSA) Level 3 compliance.

PSA Certified Level 3 is an Internet of Things (IoT) security standard that focuses on robust protection against software and hardware attacks on a chip’s root of trust. It provides an independently evaluated and validated environment that can securely house and execute the PQC algorithms.

Figure 1 PQC encompasses the replacement of Elliptic Curve Cryptography (ECC)-based asymmetric cryptography as well as increasing the size of Advanced Encryption Standard (AES) keys and Secure Hash Algorithm (SHA) sizes. Source: Infineon

“By adopting both PSA Certified Level 3 and PQC compliance with other regulations, companies can proactively address current and future cyber threats,” said Erik Wood, senior director of cryptography and product security at Infineon Technologies. He is responsible for defining the security requirements of Infineon MCUs.

Quantum computers, exponentially faster than classical computers, are still under development. However, cybercriminals can collect encrypted data now and decrypt it later using quantum computers. That calls for futureproofing of current systems to ensure that companies remain secure as quantum computing technologies advance.

Enter PQC, a collection of cryptographic algorithms designed to be secure against attacks from powerful quantum computers. In MCUs, which mainly use cryptography during boot-time and run-time operations, it commands significant changes in security architecture amid evolving regulations.

For instance, MCU’s memory size is a key design consideration. “More memory size is required because encryption keys are longer,” Wood said. “The certificate size is different because the signatures of these certificates are much bigger.”

Figure 2 PSOC Control C3 MCU’s embedded security provides stringent protection against quantum-based attacks on critical systems. Source: Infineon

Next comes the throughput shortfall. “While certificates are currently transferred through an I2C bus, the throughput falls short with QPC use,” he added. “Now you need to have three I3C buses.” Wood said that the industry is even procrastinating about whether every MCU will have a USB port in four years.

In other words, integrating QPC into MCUs will entail a primary upgrade of cryptographic algorithms. Next come memory upgrades, and finally, interface upgrades will follow.

Wood claimed that Infineon is the first MCU supplier to have integrated and ported PQC algorithms. “We offer an integrated library already hooked up to the accelerators for peak optimization and performance in a PSA-3 level device.”

Related Content

• Inside the PQC Overhaul, a Year Later
• Post-Quantum Cryptography: Moving Forward
• An Introduction to Post-Quantum Cryptography Algorithms
• Release of Post-Quantum Cryptographic Standards Is Imminent
• The need for post-quantum cryptography in the quantum decade

The post Post-quantum cryptography (PQC) knocks on MCU doors appeared first on EDN.

Reinforcement Learning (RL), unlike other machine learning (ML) paradigms, notably supervised learning, has an agent learning to act optimally within a given environment, one step at a time. At each step, it is given feedback in the form of a reward or a penalty. The goal is to learn a policy a strategy for selecting actions that maximize the total reward over a certain time horizon. There are no inputs or outputs to fit to (as in traditional supervised learning), so RL agents must balance exploring unknown actions to discover their worth and exploiting known good actions to maximize rewards.

Reinforcement Learning History:

Reinforcement learning began with behavioural psychology’s theory of behaviourism in the early 1900s. Behaviourism postulated learning as a trial and error process propelled by rewards and punishments. This concept was later adapted and formalised into computer science mathematical models that paved the way for the development of optimisation and machine learning algorithms. Reinforcement learning is akin to optimising methods where the desired function is not explicitly given but is instead hinted at through trial and error.

How does reinforcement learning work:

To enhance decision-making, reinforcement learning works by training an agent to interact with an environment. The agent gets to perform actions. After each action, the agent gets feedback in terms of rewards or penalties associated with the specific action.

Types of Reinforcement Learning:

1. Value-Based Reinforcement Learning

This method requires an agent to learn a value function that predicts the reward for performing an action in a particular state and Q-learning is the most well-known. An agent updates its Q-values in Q-learning according to the received reward and acts to maximize these Q-values.

2. Policy-Based Reinforcement Learning

Policy-based methods focus on learning the policy itself, which is the set of rules mapping states to actions, instead of estimating value functions. This is crucial in cases with complex or continuous action spaces. Methods like REINFORCE and Proximal Policy Optimization (PPO) are good examples of algorithms that follow this paradigm.

3. Model-Based Reinforcement Learning

This refers to methods which try to construct a model of the environment that can predict the following state and reward given the current state and action. Using this model, the agent can plan and make decisions ahead of time. While this method is efficient in terms of samples, its implementation can be complicated to do correctly.

4. Actor-Critic Methods 

These hybrid methods combine the strengths of value-based and policy-based approaches. The actor updates the policy based on feedback from the critic, which evaluates the action taken. This results in more stable and efficient learning, especially in complex environments.

Applications of Reinforcement Learning:

1. Self-Driving Cars

Self-driving cars use reinforcement learning to understand their surroundings. They identify the best routes, change lanes, avoid obstacles, and optimize their overall driving.

2. Automated Machines

Automated machines use reinforcement learning to master new skills like walking, picking up objects, and putting them together. As they deal with new items and different tasks, they improve how they do things in due course.

3. Medicine

Personalized treatment is now possible because of reinforcement, which allows crafting adaptive treatment plans for patients. It is also useful in optimizing clinical trials and in the management of chronic illness.

4. Investment

In portfolio management and trading, reinforcement learning technologies attempt to make investment choices by evaluating prevailing market patterns and modifying tactics geared towards greater returns.

5. Recommendation Systems

Reinforcement learning is used to improve the recommendation systems. As users interact with the content, the system learns users preferences and dynamically suggests content making the platform personalized and more engaging.

Reinforcement Learning Examples:

Reinforcement learning is integrated into numerous fields enabling the technology to thrive. In game playing, RL has enabled breakthroughs like AlphaGo which mastered complex games such as Go and chess through self-play. In autonomous driving, self-driving cars use RL to make decisions like lane changes and obstacle avoidance by learning from real and simulated environments. In robotics, RL helps machines learn tasks like walking, grasping, and assembling by adapting to physical feedback. In finance, RL algorithms optimize trading strategies and portfolio management by analyzing market data. Lastly, in recommendation systems, platforms like Netflix and Amazon use RL to suggest content or products based on user behavior, enhancing engagement and satisfaction.

Reinforcement Learning Advantages:

Reinforcement learning is adaptive and its methods are goal driven. As an example, it can be very effective in environments that are constantly changing and that require very little supervision. It is a type of learning that is guided by rewards or feedback, in which an agent learns to improve its behavior over time based on interaction with the environment.

Conclusion:

As the rest of intelligent systems, reinforcement learning is, for now, an incredible advancement and is bound to become even more so. The level of innovation that RL will bring about will be unimaginable given the availability of more processing power and much more sophisticated algorithms. Preemptive systems, self-learning autonomous agents, and machines that collaborate with humans are only the beginning. Personalized medicine, self-developing robots, and adaptive learning systems will all lean on RL technologies. These technologies will not just adapt to the world, but will actively ‘mold’ it, in essence, making the word ‘transformative’ obsolete in describing the level of change these technologies will bring.

The post Reinforcement Learning Definition, Types, Examples and Applications appeared first on ELE Times.

Infineon Technologies AG announced that its microcontrollers (MCUs) in the new PSOC Control C3 Performance Line family are compliant with Post-Quantum Cryptography (PQC) requirements for firmware protection outlined in the Commercial National Security Algorithm (CNSA) Suite 2.0. The MCUs also support PSA (Platform Security Architecture) Level 3 compliance. By complying with both standards, Infineon’s PSOC Control C3 Performance Line meets the security needs of a wide range of industrial applications and eases their transition to increased security in the PQC era.

“With the PSOC Control C3 family, we are setting a new standard for security in industrial microcontrollers, building on decades of proven experience in MCUs and secured electronic systems,” said Steve Tateosian, SVP and General Manager, IoT, Consumer and Industrial MCUs, Infineon Technologies. “Infineon is committed to meeting and evolving industry requirements for MCU embedded security that provides stringent protection against quantum-based attacks on critical systems.”

Changes in security architecture for the PQC era include the replacement of Elliptic Curve Cryptography (ECC) based asymmetric cryptography as well as increasing the size of Advanced Encryption Standard (AES) keys and Secure Hash Algorithm (SHA) hash sizes. The algorithms and implementation guidelines provided by CSNA 2.0 help to facilitate a smoother transition to Post-Quantum Cryptography.

About PSOC Control C3 family

The PSOC Control C3 family of MCUs provide real-time control for motor control and power conversion applications. New MCUs of the PSOC Control C3 Performance Line enable system performance at high switching frequencies and increase control loop bandwidth. That is achieved with proprietary autonomous hardware accelerators as well as high resolution and high performing analog peripheral support. The family supports systems designed with wide-bandgap switches while achieving best-in-class control loop frequencies, accuracy and efficiency for applications such as data centers, telecom, solar and electric vehicle (EV) charging systems.

Specific security features include support for Leighton-Micali Hash-Based Signatures (LMS), which is an efficient post-quantum cryptography FW verification algorithm integrated with SHA-2 hardware acceleration for peak performance. To maximize ease of use, Infineon’s Edge Protect Tools and ModusToolbox will support everything a customer needs to provision LMS keys as well as options for hybrid post-quantum cryptography where customers may use both LMS and ECC to sign firmware updates which can be verified by Infineon chips.

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Decision Tree Learning is a type of supervised machine learning used in classification as well as regression problems. It tries to mimic real-world decision making by representing decisions and their possible outcomes in the form of a tree. Each internal node in the tree denotes a test on a feature, each branch denotes an outcome of the test, and the leaf node gives the final decision. It is easy to understand, requires no complex data preprocessing, and is visually very informative.

Decision tree learning history:

The concept of decision trees has roots in decision analysis and logic, but their formal application in machine learning began in the 1980s. The ID3 algorithm, developed by Ross Quinlan in 1986, was one of the first major breakthroughs in decision tree learning. It introduced the use of information gain as a criterion for splitting nodes. This was followed by C4.5, an improved version of ID3, and CART (Classification and Regression Trees), developed by Breiman et al, which used the Gini index and supported both classification and regression tasks. These algorithms laid the foundation for modern decision tree models used today.

How does decision tree learning work:

Decision tree learning is a type of algorithm in machine learning where data gets split into smaller subsets and gets organized in the form of a tree. The splitting is based on the value of the data features. At the beginning, with the root node, a feature of the data gets selected. This selection feature tends to be the one that gets deemed most informative by the Gini impurity or entropy criteria. As mentioned earlier, internal nodes get to represent a certain decision rule. This process continues until the data is sufficiently partitioned or a stopping condition is met, resulting in leaf nodes that represent final predictions or classifications. The tree structure makes it easy to interpret and visualize how decisions are made step by step.

Types of Decision Trees:

1. Classification Trees

These are utilized when the dependent variable is categorical. Such trees assist in categorizing the dataset into specific categories (e.g., spam and non-spam). Each split aims to enhance class separation based on certain features.

2. Regression Trees

These trees are used when the dependent variable is continuous. Unlike categorization, these trees aim to provide numerical predictions (e.g., house prices). The split in these trees is done for minimizing prediction error.

Examples of Decision Tree Learning:

• Email Filtering: Marking emails as spam or not using keywords and sender details.
• Loan Approval: Deciding loan approval using income, credit score, and employment status.
• Medical Diagnosis: Identifying a disease with the help of symptoms and test results.
• Weather Prediction: Predicting rain using humidity, temperature, and wind speed.

Applications of Decision Tree Learning:

1. Finance

Decision trees analyze customer data and transaction behavior for credit scoring, fraud detection, and risk management.

2. Healthcare

With the use of medical records and test outcomes, they aid in disease diagnosis, treatment suggestions, and patient outcome predictions.

3. Marketing

Segmenting customers, predicting buying behavior, and optimizing campaign strategies based on demographic and behavioral data.

4. Retail

Forecasting sales, managing inventory, and personalizing product recommendations.

5. Education

Predicting student performance, dropout risk, and tailoring learning paths based on academic data.

Decision Tree Learning Advantages:

Decision Tree learning has numerous benefits, all of which contribute to its widespread use in machine learning. It is simple to grasp and analyze because the structure of the tree is akin to human decision-making and can be easily visualised. It can process both numerical and categorical data without the need for advanced data preprocessing or feature scaling. Decision trees are not affected by outliers or missing data, and they can model non-linear patterns in data. It requires very little in the way of data preparation and is immensely powerful and user-friendly because it inherently takes into account feature combinations through its hierarchical splits.

Conclusion:

Decision Tree Learning is going to mature into a dynamic, real-time intelligence system processing complex data, providing direction to autonomous systems, and enabling accountable decision-making in all sectors. These trees will, in time, become self-optimizing systems that reason, tell stories, and co-exist with human cognition, and they will serve as the ethical and intellectual foundation of future AI.

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New solution empowers engineers to meet HDMI Forum compliance standards while optimizing signal integrity and performance across high-bandwidth video applications

Keysight Technologies, Inc. announced the release of its enhanced physical layer compliance test solution for high-definition multimedia interface (HDMI), delivering robust compliance and performance validation capabilities for transmitter [source] and cable devices. The Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI addresses the growing complexity, and bandwidth demands of modern HDMI applications, including ultra-high definition (UHD) video, high dynamic range (HDR) content, and immersive audio experiences.

With the rising demand for 8K/12K video, HDR content, and high-speed connectivity, engineers face growing challenges in maintaining signal integrity across HDMI interfaces. The recent release of the HDMI 2.2 test specification by the HDMI Forum introduces more stringent compliance requirements for transmitters and cables, highlighting gaps in traditional test coverage. Without robust validation tools, manufacturers risk costly redesigns and certification delays. As HDMI technology advances, the need for comprehensive, automated test solutions is critical to ensure performance, reliability, and faster time-to-market.

As the HDMI ecosystem evolves to support higher resolutions, faster refresh rates, and increased bandwidth demands, the Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI offers a fully automated and scalable platform for professionals in design, engineering, and compliance testing to validate device performance with confidence and precision. The new test solution provides a unified platform for automated electrical testing as specified in the HDMI 2.2 test specification, ensuring that device manufacturers can confidently validate product performance at the transmission and cable, while reception testing is introduced at a later stage.

Keysight’s physical layer compliance test solution for HDMI meets the latest technical and procedural demands of the HDMI Forum. Designed for precision and efficiency, the solution integrates high-bandwidth measurement hardware with automated compliance workflows to manage complex test scenarios across transmitters and cables. The modular architecture of the solution supports flexible test configurations, while built-in diagnostics provide deep insight into the root causes of signal degradation. This enables design and validation teams to not only verify compliance but also optimize performance early in the development cycle.

Han Sing Lim, Vice President and General Manager of Keysight’s General Electronic Measurement Division, said: “With the introduction of the Keysight Electrical Performance, Validation, and Compliance Test Solution for HDMI, our customers can accelerate time-to-market for next-gen consumer electronics while ensuring robust integrity and regulatory compliance. By incorporating the latest version of HDMI technology in our solution, we are enabling leading consumer electronics designers and manufacturers to continue to push the boundaries of digital display and multimedia performance.”

Backed by Keysight’s global expertise in compliance testing and proven in high-volume production environments, the new solution delivers a trusted path to certification readiness and superior end-product performance.

The post Keysight Unveils Physical Layer Compliance Test Solution for HDMI to Meet Rising Demands for Ultra-High Resolution and High Dynamic Range appeared first on ELE Times.

Ultra-low-power RL78/L23 MCUs with segment LCD displays & capacitive touch for HMI applications

Renesas Electronics Corporation, a premier supplier of advanced semiconductor solutions, introduced the new 16-bit RL78/L23 microcontroller (MCU) group, expanding its low-power RL78 family. Running at 32MHz, the RL78/L23 MCUs combine industry-leading low-power performance with essential features such as dual-bank flash memory, segment LCD control, and capacitive touch functionality to support smart home appliances, consumer electronics, IoT and metering systems. These compact, cost-effective devices address the performance and power requirements of modern display-based human-machine interface (HMI) applications.

Ultra-Low Power Operation with Optimized LCD Performance

The RL78/L23 is optimized for ultra-low power consumption and ideal for battery-powered applications that spend the majority of time in standby. They offer an active current of just 109μA/MHz and a standby current as low as 0.365μA, along with a fast 1μs wake-up time to help minimize CPU activity. The LCD controller’s new reference mode, VL4, reduces LCD operating current by approximately 30 percent when compared to the existing RL78/L1X group. The MCUs come with SMS (SNOOZE Mode Sequencer), which enables dynamic LCD segment display without CPU intervention. By offloading tasks to the SMS, the devices minimize CPU wake-ups and contribute to system-level power savings. These innovations significantly extend battery life, simplify design and reduce replacement costs, while minimizing environmental impact.

The RL78/L23 offers a wide operating voltage range of 1.6V to 5.5V, which supports direct operation from 5V power supplies commonly used in home appliances and industrial systems. This capability reduces the need for external voltage regulators. The MCUs also integrate key components such as capacitive touch sensing, a temperature sensor, and internal oscillator, reducing BOM cost and PCB size.

Feature-Rich Peripherals for HMI Systems

Designed to meet the dynamic requirements of the HMI market, RL78/L23 integrates a suite of advanced features in a compact, cost-effective package. Its built-in segment LCD controller and capacitive touch realize sleek, responsive user interfaces for products such as induction cooktops and HVAC systems. The IH timer (Timer KB40) enables precise multi-channel heat control, which is essential in smart kitchen appliances such as rice cookers and IH cooktops. The devices include dual-bank flash memory for seamless firmware updates via FOTA (Firmware Over-the-Air), allowing continuous system operation in applications like metering, where downtime must be minimized. The dual-bank architecture allows one memory bank to run the user program, while the other receives updates. This approach keeps the system functional throughout the process for improved reliability.

“The Renesas RL78 Family of 16-bit microcontrollers has been one of the most successful products since its launch more than 10 years ago, particularly in home appliances,” said Daryl Khoo, Vice President of Embedded Processing at Renesas. “I’m pleased to announce the RL78/L23, a new generation of RL78 microcontrollers with rich features, ideally suited for smart home appliances and cost-sensitive IoT solutions. With these devices, we aim to provide a better user experience with our intuitive development environment so that customers can get to production faster with confidence, based on market-proven Renesas technologies.”

Key Features of the RL78/L23

16-bit RL78 microcontroller running at 32MHzBuilt-in segment LCD controller and capacitive touchUp to 512KB of dual-bank flash memory for seamless FOTAUp to 32KB of SRAM and 8KB of data flashSMS for ultra-low power operationIH Timer (KB40) supporting up to 3-channel induction heating controlWide operating voltage range from 1.6V to 5.5VOperating temperature range of -40°C to +105°CMultiple serial interfaces including UART, I2C, CSIIEC60730-compliant self-test library44-100-pin LFQFP, LQFP and HWQFN packages.

Intuitive Development Environment for Faster Time-to-Market

The RL78/L23 comes with an easy-to-use development environment. Developers can leverage robust support tools such as Smart Configurator and QE for Capacitive Touch to streamline system design.

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I bought inexpensive LCR meter(?) from Aliexpress $10 I don't believe testing results. These results are not 100% accurate, so please use them for reference only. It's so funny. Display is good. If you're curious, you can see a video on YouTube. https://youtu.be/lvv2YHXiezY submitted by /u/Time_Double_1213 [link] [comments]

The design of RF systems requires engineers to face complex challenges. Learn how automated tools augmented with AI are becoming available to help with this.

South Korean LED maker Seoul Semiconductor Co Ltd says that the Supreme Court of Korea has recently upheld a lower court ruling convicting Taiwan-based LED maker Everlight Electronics Co Ltd of violating the Act on Prevention and Protection of Industrial Technology for misappropriating Seoul Semiconductor’s patented LED technology....

The ongoing electrification of cars brings new trends and requirements with every new design cycle. One trend for battery electric vehicles is reducing the size of the low-voltage batteries, which power either 12-V or 48-V systems. Some auto manufacturers are even investigating whether it’s possible to eliminate low-voltage batteries completely. Regardless, you’ll need isolated high- to low-voltage DC/DC converters as a backup or buffer for the low-voltage battery rail. In all of these cases, the high-voltage battery powers the DC/DC converters. Many high-voltage battery systems in cars currently in production or in development use a 400-V or 800-V architecture.

Given the disadvantages of discharging the high-voltage battery more than necessary, high- to low-voltage DC/DC converters need to support operation with the highest possible efficiency. Different activity states in the car require different power levels in the subsystems—for example, 60 W when the driver opens the car, 300 W when the car is in standby but not moving, and 3 kW or more when the car is in drive and fully operational. It is not possible to optimize a single DC/DC converter to cover all three potential output power levels with high-efficiency operation over the whole load range; in the examples given here, you would need two or three independent power converters.

Converter topology selection

In this power tip, I will focus on the 300-W output power range, also known as a micro DC/DC converter. Suitable DC/DC topologies for this output power range include half- and full-bridge converters. Resonant topologies such as half-bridge inductor-inductor-capacitor (LLC) converters offer higher efficiency conversion than their hard-switched counterparts through zero-voltage switching (ZVS) on the primary side and zero-current switching (ZCS) on the secondary side. Another potential topology is the phase-shifted full-bridge (PSFB) topology, which also employs soft switching by leveraging ZVS but is less cost-effective for the 300-W target output power level, since it requires four switches on the primary side.

Figure 1 shows the converter efficiency for various input voltages and load values for the Texas Instruments (TI) Automotive 300 W Micro DC/DC Converter Reference Design Using Half-Bridge LLC. Optimized for 400-V battery inputs and a 48-V output, this design reflects a good compromise between efficiency and cost of the four different topologies.

Figure 1 Efficiency plot of the automotive 300-W micro DC/DC converter reference design. Source: Texas Instruments

In an electric vehicle with a 400-V architecture, the battery voltage can vary from 200 V to 450 V. In general, LLC converters are not known to work well with very wide input voltage ranges because, with peak current-mode control, such a wide input voltage range could lead to the converter prematurely entering light-load efficiency mode (also known as burst mode) under full load conditions, or reaching overload conditions too early under low input-voltage conditions. The reason for both effects is that the feedback voltage is scaled in the controller with the input voltage, making it switching frequency-dependent.

So why should you even consider an LLC for this type of application? The UCC256612-Q1 LLC controller from TI uses input-power proportional control (IPPC), which overcomes these limitations. The feedback voltage only scales with the input power, and stays quasi-constant over the whole input voltage range for a constant load current. Figure 2 shows the differences between IPPC feedback voltage behavior (Figure 2a) and traditional peak current-mode control feedback voltage behavior (Figure 2b).

Figure 2 Feedback over input voltage using (a) IPPC and (b) traditional LLC control. Source: Texas Instruments

Accurate output voltage regulation with isolation

The proper regulation of isolated power supplies in electric vehicles is a tricky topic. Optocouplers, typically used for secondary-side regulation (SSR) in nonautomotive applications, are considered unreliable in automotive applications because of aging effects on the internal glass passivation over their lifetime. An alternative way to provide output feedback to a controller on the primary side is primary-side regulation (PSR) through an auxiliary winding. PSR is not very accurate for high output currents because the voltage drop across the rectifier(s) and droop across traces to the load will be current-dependent but not visible on the auxiliary winding. A second option is to use isolated amplifiers.

For SSR, the reference design uses the TI ISOM8110-Q1 automotive-qualified pin-to-pin replacement for traditional optocoupler devices. Superior aging performance and smaller current transfer ratio (CTR) variations of the ISOM8110-Q1 enable more accurate and reliable designs, which are crucial for automotive systems with expected lifetimes of at least 10 years. In addition, the ISOM8110-Q1 has a slightly different transfer function than traditional optocouplers, enabling higher control loop bandwidths that can ultimately save costs because lower output capacitance values will be able to meet similar load transient requirements.

Figure 3 shows a load transient from 3 A to 6.25 A and back to 3 A for the reference design with a 48-V output. The output voltage deviation with four 82-µF output capacitors is only 400 mV.

Figure 3 Load transient behavior, 400 VIN, 3 A to 6.25 A, and back to 3 A. Source: Texas Instruments

Apart from dynamic output accuracy, load regulation under static load conditions is important too. Figure 4 shows the load regulation across different input voltages for the reference design.

Figure 4 Load regulation over various input voltage levels, illustrating good load regulation under static load conditions. Source: Texas Instruments

For full functionality, the ISOM8110-Q1 requires a bias current of at least 700 µA on the diode side of the device and 700 µA multiplied by the worst-case CTR on the transistor side, which is 155% with a 5 mA bias current and 180% with a 2 mA bias current. Because some control ICs are optimized for minimum standby power, the feedback pin of such a controller might not be capable of sourcing sufficient current to supply the ISOM8110-Q1 on its own. A simple workaround for such a scenario is to provide the bias current with a pull-up resistor from a regulated voltage rail to the feedback pin. The UCC256612-Q1 generates a 5-V rail with an internal low-dropout regulator, which is externally accessible and can therefore provide the bias current for the opto-emulator IC. The block diagram in Figure 5 demonstrates the implementation of this workaround.

Figure 5 Secondary-side feedback implementation using the ISOM8110-Q1, with external bias from a control IC on the primary side. Source: Texas Instruments

Alternative for micro DC/DC converters

The reference design demonstrates that the half-bridge LLC topology can be a viable alternative for automotive micro DC/DC converters in the 300 W power range, demonstrating good efficiency as well as excellent static and dynamic output voltage regulation.

The ISOM8110-Q1 is a cost-effective, accurate and reliable option to close the loop of isolated power converters in automotive applications. It works well with controllers optimized for low standby power when there is the possibility of an external bias voltage.

Markus Zehendner is a systems engineer and Member Group Technical Staff in TI’s EMEA Power Supply Design Services group. He holds a bachelor’s degree in electrical engineering and a master’s degree in electrical and microsystems engineering from the Technical University of Applied Sciences in Regensburg, Germany. His main focus lies on automotive low-voltage designs for advanced driver assistance systems and infotainment, as well as high-voltage designs for hybrid and electric vehicle applications.

 Related Content

• Power Tips #75: USB Power Delivery for automotive systems
• Power Tips #118: Using interleaved ground planes to improve noise filtering from isolated power supplies
• Power Tips #101: Use a thermal camera to assess temperatures in automotive environments
• Power Tips #104: Design considerations for boosting power density in infotainment
• Power Tips #129: Driving high voltage silicon FETs in 1000-V flybacks

The post Power Tips #144: Designing an efficient, cost-effective micro DC/DC converter with high output accuracy for automotive applications appeared first on EDN.

Старт набору студентів на онлайн-курси проєкту TANDEM-UA-DE в зимовому семестрі 2025/2026 (реєстрація до 09.09.2025) kpi ср, 08/27/2025 - 16:49

💢 Сесія професорсько-викладацького складу 2025 kpi ср, 08/27/2025 - 15:33

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Anand Kumar is the Managing Director of STMicroelectronics (ST), India, and has held this position since June 2025. He heads the Global IP and Library Design team, located across France and India, within ST’s Global Technology R&D organization. Kumar’s team takes IPs from concept to production maturity and brings variants over time, across the full portfolio of ST’s proprietary, differentiated technologies and technology platforms.

Anand Kumar began his career in 1999 as an Analog Designer in ST’s Central R&D organization. With over 25 years of experience as a designer, design team lead, and global lead of IP design, he has deep expertise in designing Analog and Mixed-Signal IPs and leading high-performing IP and Library Design teams delivering a wide range of IPs. These include memories, IOs, standard cells, clock-generation IPs, data converters, sensors, power-management IPs, fuses, and other analog and digital IPs across all ST technology nodes.

Kumar holds more than 20 patents and has presented at multiple top-tier international scientific and technical semiconductor conferences.

Anand Kumar was born in Narwana (Haryana state), India, in 1976, and holds a degree in Electronics and Communication Engineering from NSUT (Netaji Subhas University of Technology, formerly Delhi Institute of Technology), Delhi.

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Specialty analog foundry Tower Semiconductor Ltd of Migdal Haemek, Israel and Xscape Photonics Inc of Fort Lee, NJ and Santa Clara, CA, USA (which is funded by industry leaders such as NVIDIA and Cisco, and develops silicon photonics interconnects for AI data centers) have announced the successful prototyping and validation kit availability of what is claimed to be the industry’s first on-chip, optically pumped, multi-wavelength laser source. Built on Tower’s mature, high-volume PH18 silicon photonics platform, the solution supports CWDM and DWDM wavelength grids and is tailored for AI data-center fabrics, where bandwidth density, power efficiency and scalability are essential...