Microelectronics world news

The growth of electronics manufacturing in India is unprecedented, having emerged out as one of the leading industries with a market size of ₹11.5 lakh crore, Union Minister for Electronics and Information Technology Ashwini Vaishnaw stated.

Addressing a press conference at the BJP headquarters, the minister said production in the sector has increased almost six times in the last 11 years, while exports have increased eight times. This growth, he said, has also created direct and indirect employment opportunities for more than 25 lakh people across the country.

The Minister explained how India has moved towards self-reliance in electronics and can almost make any component that goes inside devices like mobile phones. “From glass and casings to chips, PCBs, and camera modules, all are being manufactured domestically,” he added.

Highlighting a new plant set up in Sohna, Haryana, the minister added that the facility will be manufacturing smartphone batteries. The plant alone is expected to produce nearly 20 crore battery packs, helping meet the nation’s demand of around 50 crore units annually.

According to the minister, the momentum of Swadeshi is forming, and a higher acceptance will build up the spirit of a self-reliant India. Encouraging people to purchase Indian goods, he said.

From the economic perspective, Vaishnaw associated the growth of the sector with the recent GST rationalisation, which has simplified tax slabs and lowered rates for household electronics.  He said that consumers can expect lower prices in the near future on TVs, refrigerators, microwave ovens, dishwashers and power banks.

“With the GST reforms, Prime Minister Modi has given considerable relief to the middle-class families by easing the tax burden on daily essentials as well as household appliances,” said the minister.

The post Electronics Manufacturing Surges to ₹11.5 Lakh Crore Industry, Generates 25 Lakh Jobs: Vaishnaw appeared first on ELE Times.

Mr Ken Paxton,  Director, Advanced Semiconductor, DigiKey Ken Paxton serves as the director of advanced semiconductor at DigiKey, where he leads the company’s relationships with top semiconductor suppliers. His team is dedicated to building supplier partnerships and creating innovative market strategies that support mutual growth. Ken’s focus is to help suppliers position their products to meet the latest customer needs, ensuring fast market introduction and connecting them with DigiKey’s broad network of engineers and designers.

 

“We are enhancing our logistics to offer products manufactured in India and distributed directly within the country, using local currency,” says Ken Paxton, Director of Advanced Semiconductor for DigiKey, in an exclusive conversation with the ELE Times. As the industries across the globe embrace AI either as agents or as a tool, it becomes more important than ever to check how the companies empowering this revolution are themselves adopting the change. Furthermore, as India’s semiconductor ambitions soar high and the country marches to gain a good piece of the global semiconductor market, ELE Times takes the hot seat with DigiKey’s Ken Paxton to unravel the AI-specific and India-specific strategy of the world’s 5th largest electronics component distributor.

Distributors and AI

“We’ve embedded AI capabilities across DigiKey, not as isolated projects, but as an integrated approach to innovation. We’re using AI to help us make better, faster, and more accurate decisions, serving customers and suppliers more efficiently and effectively,” Mr. Paxton says in response to the AI question. He underlines that to DigiKey, AI is not just about financial parameters but a fundamental commitment to reimagining what is possible. 

He further underlines DigiKey’s ultimate vision to be an AI-driven innovator where customer experiences shape the future of the industry. 

Underlining India’s commitments to semiconductor growth and innovations are the India Semiconductor Mission (ISM) with an outlay of Rs 76,00 Crore and the Design Linked Incentive (DLI) with an outlay of Rs 1,000 Crore, to name a few. Among the schemes, the DLI scheme caters specifically to the design engineers with an aim to foster the semiconductor design ecosystem in India. To that effect, it is important to ensure that the design engineers are provided with the components they need on time with quality assurance. 

That’s where DigiKey comes into India’s semiconductor foray. 

“India represents a strategic growth region for DigiKey, with a dynamic engineering community that can capitalize on our robust stocking positions,” Ken Paxton says. Recognizing India’s design potential, Mr. Paxton underlines DigiKey’s close collaboration with the Indian supplier community. He says that the collaboration helps DigiKey in getting a comprehensive understanding of manufacturing capabilities and logistics specific to the Indian market. 

DigiKey’s India Approach 

As the Indian market evolves and the government as well as the private sector get on the nerves, it’s important to track how the distribution giants like DigiKey are responding to the same. To this, Mr. Paxton says, “DigiKey is constantly monitoring evolving opportunities in supplier distribution areas that could help enhance our reach within India to grow the electronics and technology market,” indicating a positive and uplifting scenario for the prospects of the industry. 

He also goes on to add about the various other initiatives of DigiKey that aim to empower the design engineers community in India. “Beyond the products that drive technological innovation, DigiKey provides engineers with robust digital solutions, tools, and technical content to enhance their work,” he adds. 

Emerging Technologies 

Apart from adopting the emerging technologies like AI and automation in internal functioning, DigiKey also purveys the newest technologies and components for building the machines and devices that lead the future. It can range from sectors like healthcare and AI to energy, industrial automation, sustainability, and IoT. “We specialize in maintaining robust inventory across virtually all emerging technologies offered by our suppliers,” adds Mr. Paxton. 

India-Specific Customization 

Sharing an India-specific example, Mr. Paxton spotlights one instance of their collaboration with a supplier that has recently relocated its manufacturing operations to India. Consequently, DigiKey is enhancing its logistics to offer products manufactured in India and distributed directly within the country, using local currency. Mr. Paxton explained that DigiKey has been engaging with suppliers on developing products tailored to regional technical requirements. 

He further explained that the company also provides power products in formats beyond the US standard to ensure Indian users enjoy a seamless, out-of-the-box experience. 

Focus on India. 

Mr. Paxton explained that India and the broader APAC region will continue to remain a priority for DigiKey, given the strong concentration of technological advances emerging from this part of the world. He further explained that the company’s focus in the region also extends to education and maker products, with an aim to provide cutting-edge technology in user-friendly formats to support aspiring engineers. He added that DigiKey will continue to partner with and promote suppliers committed to these areas.

“Engineering activity is high, and innovation is strong as we move into the second half of the year. We’re thrilled to help support our suppliers as they bring NPIs to market,” says Mr. Paxton as he wraps up the conversation.

India’s semiconductor ambitions are backed by initiatives like the ₹76,000 crore ISM and the ₹1,000 crore DLI scheme, which focuses on fostering a strong design ecosystem. A critical part of this effort is ensuring design engineers get timely access to quality components.   To highlight how distributors are enabling this, we present our exclusive series — “Powering the Chip Chain” — featuring conversations with key industry players.

 

The post Exclusive Feature, Part 1: “We’re Using AI to Help Us Make Better, Faster, and More Accurate Decisions,” says DigiKey’s Ken Paxton appeared first on ELE Times.

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In this article, we explore the limitations of LC oscillators for direct FM generation and how to deal with them using multipliers and automatic frequency control (AFC) circuits.

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After a long time trying to make a circuit board in house for a QFN package, I have a working ATtiny 841 blinking an LED. QFN unlocked!! submitted by /u/LavandulaTrashPanda [link] [comments]

The expanded series offers higher working voltages to 1000 V in smaller form factors.

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Join your engineering community peers and take our EETech Engineering Insights Survey 2025. You’ll get a summary of the results once they’re released. And you’ll be entered for a chance to win an Amazon card prize!

I bought this display from Alibaba, and then created PCB with JLCPCB. Refresh rate 60Hz with STM32. submitted by /u/WezJuzSieZamknij [link] [comments]

For decades, I have used my IEEE alias address for both incoming and outgoing emails with no difficulties; however, this is no longer the case. The IEEE alias address is no longer workable for outgoing e-mails that are destined for any “gmail.com” recipient.

If I put ambertec@ieee.org in the “From” line of such an outgoing message, I get an immediate message rejection reply that looks like this:

If the content of the “From” line does not match the actual sending address, rejection occurs. In this case, the intended recipient was my own cell phone, but this kind of message comes my way when trying to send any email to any Gmail.com user.

I have neither the time nor the energy to wade into the bureaucratic techno-drivel of the “DMARC policy” or of the “DMARC initiative.” I simply cite my own experience as a signal that you and other IEEE members who read this will know that you are not alone.

John Dunn is an electronics consultant and a graduate of The Polytechnic Institute of Brooklyn (BSEE) and of New York University (MSEE).

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The post An e-mail delivery problem, Part 2 appeared first on EDN.

At the International Symposium on Automotive Lighting (ISAL 2025) in Darmstadt, Germany (22–24 September), Nichia Corp of Tokushima, Japan — the world’s largest gallium nitride (GaN)-based light-emitting diode/laser diode (LED/LD) manufacturer and inventor of high-brightness blue and white LEDs — is presenting its portfolio of Pixelated Light Source (PLS) solutions for adaptive driving beam (ADB) headlamps...

Lumileds of Amsterdam, The Netherlands says that its new LUXEON HL1Z Color Line is a high-power, chip-scale package (CSP) portfolio of 10 direct and phosphor-converted colors that share a common focal length and footprint. Offered in small color bins and with narrow forward-voltage bins, the LEDs are said to deliver better color uniformity, better efficacy and thermal management and more consistent behavior at operating conditions...

Compound Semiconductor Applications (CSA) Catapult has appointed Caroline O’Brien as its new chief executive officer, joining in late September...

Decision Tree Learning’s architecture is a tree-like, hierarchical structure employed in both classification and regression in supervised machine learning. It starts with a root node, which is the complete dataset and the starting point of the first split according to a chosen feature. From there, the tree splits into internal decision nodes, where each of them is a test on an attribute, and branches, which represent the results of those tests. The algorithm repeats recursively, splitting the data into subsets until reaching leaf nodes, where the terminal output is represented as either a class label or a numeric value.

Such an architecture is constructed based on algorithms such as CART (Classification and Regression Trees), which select the optimal splits based on evaluation of criteria such as Gini impurity or entropy. The aim is to produce a model imitating human decision-making by posing a series of questions that progressively give more particular conclusions. The ease of interpretability and simplicity of this organization make decision trees widely used in predictive analytics and data mining.

Types of Decision Tree Learning:

1. Classification Tree

A Classification Tree is created to solve problems where the output variable is a category, i.e., it is a member of a particular class or category. The tree divides the data based on feature values that can best distinguish the categories. For each node, the algorithm selects the feature that gives the maximum information gain or decreases Gini impurity most. This goes on until the data has been separated into pure subsets, or leaf nodes, which are the final class prediction. For instance, it can distinguish between emails and non-spams.

2. Regression Tree

A Regression Tree is employed when the target variable is continuous, i.e., it has numerical values. Rather than dividing data into categories, it estimates a numeric value by averaging the values in each leaf node. The tree splits data according to features that reduce the variance or mean squared error in the target variable. Each split is designed to produce subsets as homogeneous as possible based on the numerical output. For example: Predicting house prices, forecasting sales.

Decision Tree Learning Diagram:

The figure shows the decision tree learning architecture, a supervised learning machine model that applies to both classification and regression learning. At the apex is the root node, which symbolizes the entire data set and makes the first decision based on a chosen feature. It divides into internal nodes, each symbolizing a point of decision which further divides the data along specific feature values. These internal nodes subsequently branch out to leaf nodes that give the terminal output either a class label for a classification tree or a numerical value for a regression tree. The left half of the diagram illustrates a classification tree where the decisions end up in discrete categories, and the right half illustrates a regression tree where the outputs are continuous values. The framework is binary and symmetrical, highlighting the way data recursively splits in order to make a predictive judgment. The graphical framework serves to demystify how decision trees work through the gradual elimination of possibilities based on feature divisions.

Conclusion:

Decision trees are not only model they’re reasoning frameworks. Their readability, interpretableness, and flexibility make them a starting point for data science, particularly when understanding and actionable results are paramount. From classifying emails to forecasting real estate values, decision trees provide a step-by-step, logical process to comprehend the data.

The post Decision Tree Learning Architecture Definition, Types and Diagram appeared first on ELE Times.

Electric traction has become a critical part of a growing number of systems that need efficient motion and position control. Motors do not just provide the driving force for vehicles, from e-bikes to cars to industrial and agricultural machinery. They also enable a new generation of robots, whether they use wheels, propellers or legs for motion.

The other common thread for many of these systems lies in the way they are expected to operate in a highly connected environment. For instance, wireless connectivity has enabled novel business models for e-bike rental and delivers positioning and other vital data to robots as they move around.

But the same connections to the Internet open avenues of attack in ways that previous generations of motion-control systems have not had to deal with. It complicates the tasks of designing, certifying, and maintaining systems that ensure safe operation.

To guarantee the actuators do not cause injury, designers must implement safeguards for their control systems to prevent them being bypassed and creating unsafe situations. They also need to ensure that corruption by hackers does not disrupt the system’s behavior. Security, therefore, now plays a major role in the design of the motor-control subsystems.

Figure 1 Connectivity in warehouse robots also opens vulnerabilities in motor control systems. Source: EnSilica

Algorithmic demands drive architectural change

Complexity in the motor control also arises from the novel algorithms that designers are using to improve energy efficiency and to deliver more precise positioning. The drive algorithms have moved away from simple strategies such as analog controllers that simply relate power delivered to the motor windings to the motors rotational speed.

They now employ far more sophisticated techniques such as field-oriented control (FOC) that are better able to deliver precise changes in torque and rotor position. With FOC, a mathematical model predicts with high precision when power transistors should activate to supply power to each of the stator windings in order to control rotor torque.

The maximum torque results when the electric and magnetic fields are offset by 90°, delivering highly efficient motion control. It also ensures high positioning accuracy with no need for expensive sensors or encoders. Instead, the mathematical model uses voltage and current inputs from the motor winding to provide the data needed to estimate position and state accurately.

Figure 2 The use of techniques like FOC delivers highly efficient motion control, which ensures greater positioning accuracy without expensive sensors or encoders. Source: EnSilica

In robotics, these algorithms are being supplemented by techniques such as reinforcement learning. Using machine learning to augment motion control has proven highly effective at delivering precise traction control for both wheeled vehicles and legged robots. Dusty or slippery surfaces can be problematic for any automated traction control systems. Training the system to cope with these difficult surfaces delivers greater stability than conventional model-based techniques.

Such control strategies often call for the use of extensive software-based algorithms running on digital signal processors (DSPs) and other accelerators alongside high-performance microprocessors in a layered architecture because of the different time horizons of each of the components.

An AI model trained using a reinforcement learning model, for example, will typically operate with a longer cycle time than the FOC algorithms and the pulse-width modulation (PWM) control signals below them that ensure the motors follow the response needed. As a result, DSP-based models with long time horizons will be supported by algorithms and peripherals that use hardware assistance to operate and meet the deadlines required for real-time operation.

The case for custom hardware

The hard real-time functions are those that have direct control over the power transistors that deliver power to the motor windings, usually implemented in an “inverter” comprising a half-bridge circuit for each of the motor phases. Traditionally, such half-bridge controllers have focused on the implementation of timing loops for PWM.

The switching frequencies are often too high to be supported reliably by software running even on a dedicated microprocessor without needing the processor to be clocked at excessive frequencies. The state machines used to implement PWM switching also take care of functions such as dead-time insertion, which is used to ensure that each transistor doesn’t turn on before its counterpart transistor in the half-bridge inverter is turned off.

The timing gap prevents the shoot-through of current that would result if both transistors were active at the same time. The excess current can damage the motor windings and the drive circuit board. These subsystems are so important that they are often provided as standard building blocks for industrial microcontrollers.

However, in the context of increased threats from hackers and the need to support advanced algorithms, the inverter controller can become a vital component in supporting overall system resilience. By customising the inverter controller, implementors can more easily guarantee safety and security, as well as protect core traction-control IP. Partitioning of the inverter and the rest of the drive subsystem need not just support all three aims, which can also reduce the cost of implementation and verification.

A major advantage of hardware in terms of security is its relative immutability compared to software code. Attackers cannot replace important parts of the hardware algorithm if they gain access. This simplifies some aspects of security certification in addition. Techniques such as formal verification can determine whether the circuitry can ever enter a particular state. Future updates to the system will not directly affect that circuitry.

It’s possible for code changes to alter the interactions between the microcontroller-based subsystems and the lower-level hardware. However, this relationship provides opportunities for the designer to improve their ability to guarantee safe operation, even under the worst-case conditions where a hacker has gained access and replaced the firmware.

Hardware-based lockout mechanisms and security checks can ensure that if the upper-level software of the system is compromised, the system will place itself into a safe state. The lockouts can include support for mechanisms such as secure boot. This ensures that only the software that passes the ASIC’s own checks can activate the motor.

Using hardware for safety and security protection can help reduce the cost of software assurance, which is now subject to legislation such as the European Union’s Cybersecurity Resilience Act (CRA). The new law demands that manufacturers and service operators issue software updates for critically compromised systems.

By moving key elements of the system design into hardware and minimizing the implications of a hack, the designer can reduce the need for frequent updates if new vulnerabilities are found in upper-level software. Similarly, moving interlocks into hardware simplifies the task of demonstrating safe operation for standards such as ISO 26262 compared with purely software-based implementations.

Physical attacks can often involve power interruptions, which provides a way to design an ASIC that protects against such tampering. For example, if power monitoring circuitry detects a brownout, it can reset the microprocessor and place the rest of the system in a safe, quiescent state.

Hardware choices that support compliance and control

Alongside the additional functions, an ASIC inverter controller can host more extensive parts of the motor-control subsystem and reduce the cost of the microprocessor components. For example, FOC relies on trigonometric and other computationally expensive transforms.

Moving these into a coprocessor block in the ASIC can streamline the design. This combination can also reduce control latency by connecting inputs from current and voltage sensors to the low-level DSP functions.

The functions need not all be fixed. Modern ASICs may include configurable blocks such as programmable filters, gain stages, and parameterizable logic to offer a level of adaptability. The use of programmable functions can let a single ASIC design control various motor configurations across an entire product range.

The programming of these elements illustrates one of the many safety and security trade-offs that design teams can make. Incorporating non-volatile memory into the ASIC can provide the greatest security. Putting the programmable elements into an ASIC that can be locked by blowing fuses after manufacturing is more secure than a design where a host microcontroller writes configuration values during the boot process.

The MCU-based control chips require a silicon process suitable for storing the firmware code, normally based on flash memory. This implies some additional processing masks, which increase the cost of the final product, a factor especially sensitive if the production volume is high.

If the design calls for the high-voltage capability offered by Bipolar-CMOS-DMOS (BCD) processes for the motor-drive circuitry, a second die may be needed for non-volatile memory. But the flash CMOS process will normally support a higher logic density than the BCD-based parts, which allows the overall cost to be optimized.

Thanks to its ability to support deterministic control loops and support verification techniques that can ease security and safety certification, the use of hardware is becoming increasingly important to e-mobility and robotics designs.

Through careful architecture selection, such hardware can enable the use of software for flexibility and its own ability to support novel control strategies as they evolve. The result is an environment where ASIC use can offer the best of both worlds to design teams.

David Tester, chief engineer at EnSilica, has 30+ years of experience in the development of analogue, digital and mixed-signal ICs across a wide range of semiconductor products.

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Designed for Automotive, Energy, and Industrial Applications, AEC-Q200 Qualified Device Withstands Grade III THB Testing

Vishay Intertechnology, Inc. introduced a new AEC-Q200 qualified DC-Link metallized polypropylene film capacitor designed for the harsh conditions of automotive, energy, and industrial applications. Offering high temperature operation up to +125 °C, the Vishay Roederstein MKP1848e delivers ripple current up to 44.5 A and withstands temperature humidity bias (THB) in accordance with Grade III of IEC60384-16 ed.3 – 60 °C / 93 % R.H for 1344 hours at rated voltage.

With its high temperature operation and resistance to high humidity, the Automotive Grade capacitor released, is ideal for automotive power conversion applications such as on-board chargers (OBC), power trains, HVAC systems, e-compressors, and DC/DC converters. This next-generation DC-Link capacitor also addresses the stringent needs for energy and industrial power conversion applications such as fast chargers, solar inverters, rectifiers for hydrogen electrolyzers, battery storage systems, motor drives, and UPS.

The MKP1848e offers rated capacitance from 1 µF to 140 µF and low ESR down to 1.0 mΩ, in rated voltages from 500 VDC to 1300 VDC. The devices provide 25 % higher ripple current density than previous-generation solutions with the same volume, while its compact footprint and pitch options down to 22.5 mm enable volume reductions up to 40 % and 15 %, respectively, at 500 VDC and 900 VDC.

To meet the standard high voltage levels of electric (EV) and plug-in hybrid electric vehicles (PHEV), the MKP1848e withstands operating voltages from 250 VDC to 800 VDC at +125 °C for a limited time. It also features high thermal shock capabilities — withstanding 1000 temperature cycles from -40 °C to +125 °C, with a 30-minute dwell time for each temperature extreme.

The post Vishay Intertechnology Automotive Grade MKP1848e DC Link Film Capacitor Delivers High Temp. Operation Up to +125 °C and High Robustness Under High Humidity appeared first on ELE Times.