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Awareness of LiDAR and advanced laser technologies has grown significantly in recent years. This is in no small part due to their use in autonomous vehicles such as those from Waymo, Nuro, and Cruise, plus those from traditional brands such as Volvo, Mercedes, and Toyota. It’s also making its way into consumer applications; for example, the iPhone Pro (12 and up) includes a LiDAR scanner for time-of-flight (ToF) distance calculations.

The potential of LiDAR technologies extends beyond cars, including applications such as range-finding in golf and hunting sights. However, the nature of the technology used to power all these systems means that solutions currently on the market tend to be bulkier and more power-intensive than is ideal. Even within automotive, the cost, power consumption, and size of LiDAR modules continue to limit adoption.

Tesla, for example, has chosen to leave out LiDAR completely and rely primarily on vision cameras. Waymo does use LiDAR, but has reduced the number of sensors in its sixth-generation vehicles: from five to four.

Overcoming the known power and size limitations in LiDAR design is critical to enabling scalable, cost-effective adoption across markets. Doing so also creates the potential to develop new application sectors, such as bicycle traffic or blind-spot alerts.

In this article, we’ll examine the core technical challenges facing laser drivers that have tended to restrict wider use. We’ll also explore a new class of laser driver that is both smaller and significantly more power efficient, helping to address these issues.

Powering ToF laser drivers

The main power demand within a LiDAR module comes from the combination of the laser diode and its associated driver that together generate pulsed emissions in the visible or near-infrared spectrum. Depending on the application, the LiDAR may need to measure distances up to several hundred meters, which can require optical power of 100-200 W. Since the efficiency of the laser diodes is typically 20-30%, the peak driving power delivered to the laser must be around 1 kW.

On the other hand, the pulse duration must be short to ensure accuracy and adequate resolution, particularly for objects at close distances. In addition, since the peak optical power is high, limiting the pulse duration is critical to ensure the total energy conforms to health guidelines for eye safety. Fulfilling all these requirements typically calls for pulses of 5 ns or less.

Operating the laser thus requires the driver to switch a high current at extremely high speed. Standing in the designer’s way, the inductance associated with circuit connections, board parasitics, and bondwires of IC packages is enough to prevent the current from changing instantaneously.

These small parasitic inductances are intrinsic to the circuit and cannot be eliminated. However, by introducing a parallel capacitance, it is possible to create a resonant circuit that takes advantage of this inductance to achieve a short pulse duration. If the overall parasitic inductance is about 1 nH and the pulse duration is to be a few nanoseconds, the capacitance can be only a few nano Farads or less. With such a low value of capacitance, the applied voltage must be on the order of 100 V to achieve the desired peak power in the laser. This must be provided by boosting the available supply voltage.

Discrete laser driver

Figure 1 shows the circuit diagram for a resonant laser-diode driver, including the resonant capacitor (Csupply) and effective circuit inductance (Lbond). A boost regulator provides the high voltage needed to operate the resonant circuit.

Figure 1 Resonant gate driver and boost regulator, including the resonant capacitor (Csupply) and effective circuit inductance (Lbond). (Source: Silanna Semiconductor)

The circuit requires a boost voltage regulator, depicted as Boost voltage regulator (VR) in the diagram, to provide the high voltage needed at Csupply to deliver the required energy. The circuit as shown contains a discrete gate driver for the main switching transistor (FET), which must be controlled separately to generate the desired switching signals.

In addition, isolation resistance is needed between Cfilter and Csupply, shown in the diagram, to ensure the resonant circuit can operate properly. This is relatively inefficient, as no more than 50% of the energy is transferred from the filter side to Csupply.

Handheld equipment limitations

In smaller equipment types, such as handheld ranging devices and action cameras, the high voltage must be derived from a small battery of low nominal voltage—typically a 3-V CR2 or a 3.7-V (nominal voltage, up to 4.2 V) lithium battery—which is usually the main power source.

Figure 2 shows a comparable schematic for a laser-diode driver powered from a 3.7-V rechargeable lithium battery. Achieving the required voltage using a discrete boost VR and laser-diode driver is complex, and designers need to be very careful about efficiency.

Multiple step-up converters are often used, but efficiency drops rapidly. If two stages are used, each with an efficiency of 90%, the combined efficiency across the two stages is only 81%.

Figure 2 A laser driver operated from a rechargeable lithium battery, two stages are used for a combined efficiency of 80%. (Source: Silanna Semiconductor)

In addition, there are stringent constraints on enclosure size, and the devices are often sealed to prevent dust or water ingress. On the other hand, sealing also prevents cooling airflow, thereby making thermal management more difficult. In addition, high overall efficiency is essential to maximize battery life while ensuring the high optical power needed for long range and high accuracy.

Circuit layout and size

The high speeds and slew rates involved in making the LiDAR transmitter work call for proper consideration of circuit layout and component selection. A gallium nitride (GaN) transistor is typically preferred for its ability to support fast switching at high voltage compared to an ordinary silicon MOSFET. Careful attention to ground connections is also required to prevent voltage overshoots and ground bounce from disrupting proper transistor switching and potentially damaging the transistor.

Also, a compact module design is difficult to achieve due to efficiency limitations and thermal management challenges. The inefficiencies in the discrete circuit implementation mean operating at high power produces high losses and increased self-heating that can cause the operating temperature to rise. However, while short pulses can reduce the average thermal load, current slew rates must be extremely high. If this cannot be maintained consistently, extra losses, more heat, and degraded performance can result.

A heatsink is the preferred thermal management solution, although a large heatsink can be needed, leading to a larger overall module size and increased bill of materials cost. In addition, ensuring eye safety calls for a fast shutdown in the event of a circuit fault.

Bringing the boost stage, isolation, GaN FET driver, and control logic into a single compact IC (see Figure 3) achieves greater functional integration and offers a route to higher efficiency, smaller form factors, and enhanced safety through nanosecond-level fault response.

Figure 3 An integrated driver designed for resonant capacitor charging combines short pulse width with high power and efficiency. This circuit was implemented with Silanna SL2001 dual-output driver. (Source: Silanna Semiconductor)

While leveraging resonant-capacitor charging to achieve short, tightly controlled pulse duration, this integration avoids the energy losses incurred in the capacitor-to-capacitor transfer circuitry. The fault sensing and reporting can be brought on-chip, alongside these timing and control features.

This approach is seen in LiDAR driver ICs like the Silanna FirePower family, which integrate all the functions needed for charging and firing edge-emitting laser (EEL) or vertical-cavity surface-emitting laser (VCSEL) resonant-mode laser diodes at sub-3-ns pulse width. Figure 4 shows how an experimental setup produced a 400-W pulse of 2.94 ns, operating with a capacitor voltage boosted to 120 V with a resonant capacitor value of 2.48 nF.

Figure 4 Test pulse produced using integrated driver and circuit configuration as in Figure 3. (Source: Silanna Semiconductor)

The driver maintains control of the resonant capacitor energy and eliminates any effects of input voltage fluctuations, while on-chip logic sets the output power and performs fault monitoring to ensure eye safety. The combined effects of advanced integration and accurate logic-based control can save 90% of charging power losses compared to a discrete implementation and realize an overall charging efficiency of 85%. The control logic and fault monitoring are configured through an I2C connection.

Of the two devices in this family, the SL2001 works with a supply voltage from 3 V to 24 V and provides a dual GaN/MOS drive that enables peak laser power greater than 1000 W with a pulse-repetition frequency up to several MHz. The second device, the SL2002, is a single-channel driver targeted for lower power applications and is optimized for low input voltage (3 V-6 V) operation. Working off a low supply voltage, this driver’s 80-V laser diode voltage and 1 MHz repetition rate are suited to handheld applications such as rangefinders and 3D mapping devices. Figure 5 shows how the SL2002 can simplify the driving circuit for a battery-operated ranging device powered from a 3.7 V lithium battery.

Figure 5 Simplified circuit diagram for low-voltage battery-operated ranging. (Source: Silanna Semiconductor)

Shrinking LiDAR modules

LiDAR has been a key component in the success of automated driving, working in conjunction with other sensors, including radar, cameras, and ultrasonic detectors, to complete the vehicle’s perception system. However, LiDAR modules must become smaller and more energy-efficient to earn their place in future vehicle generations and fulfil opportunities beyond the automotive sphere.

Focusing innovation on the laser-driving circuitry unlocks the path to next-generation LiDAR that is smaller, faster, and more energy-efficient than before. New, single-chip drivers that deliver high optical output power with tightly controlled, nanosecond pulse width enable LiDAR to address tomorrow’s cars as well as handheld devices such as rangefinders.

Ahsan Zaman is Director of Marketing at Silanna Semiconductor, Inc. for the FirePowerTM Laser Drivers line of products. He joined the company in 2018 through the acquisition of Appulse Power, a Toronto, Canada-based Startup company for AC-DC power supplies, where he was a co-founder and VP of Engineering. Prior to that, Ahsan received his B.A.Sc., M.A.Sc., and Ph.D. degrees in Electrical Engineering from the University of Toronto, Canada, in 2009, 2012, and 2015, respectively. He has more than a decade of experience in power converter architectures, mixed-signal IC design, low-volume and high-efficiency power management solutions for portable electronic devices, and advanced control methods for high-frequency switch-mode power supplies. Ahsan has previously collaborated with industry-leading semiconductor companies such as Qualcomm, TI, NXP, EXAR etc., and co-authored more than 20 IEEE conference and journal publications, and holds several patents in this field

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Publicly traded mineral exploration company Germanium Mining Corp of Vancouver, BC, Canada has been accepted as a new member of the US National Defense Industrial Association (NDIA), which supports collaboration across industry, government and academia to strengthen US national security and the defense industrial base. Germanium Mining Corp is already a member of the Nevada Mining Association...

STMicroelectronics held a virtual media briefing, hosted by Patrick Aidoune, General Manager, General Purpose MCU Division at ST, on November 17, 2025. The briefing was held before their flagship event, the STM32 Summit, where they launched STM32V8, a new generation of STM32 microcontrollers.

STMicroelectronics introduced its new generation microcontroller, STM32V8, under the STM32 class recently. Built on an innovative 18nm process technology with FD-SOI and phase change memory (PCM) technology included, this microcontroller is the first of its kind in the world.  It is the first under 20nm process to use FD-SOI along with an embedded PCM technology.

FD-SOI Technology

The FD-SOI is a silicon technology, co-developed by ST, which brought innovation in the aerospace and automotive applications. The 18nm process, co-developed with the Samsung Foundry, provides a cost-competitive leap in both performance as well as power consumption.

The FD-SOI technology gives a strong robustness to ionising particles and reliability in harsh operating environments, which makes it particularly suitable for intense radiation exposure found in earth orbit systems. The FD-SOI also helps reduce the static power consumption, along with allowing operations on a lower voltage supply, while sustaining harsh industrial environments as well.

Key Features

STM32V8’s Arm Cortex-M85 core, along with the 18nm process, gives it a clock speed of up to 800MHz, making it the most powerful STM32 ever shipped. It has also been embedded with up to 4 Mbytes of user memory in a competitive dual bank, allowing bank swapping for seamless code updates.

Keeping in mind the needs of developers, the STM32V8 provides for more compute headroom, along with more security and improved efficiency. Compared it is 40nm process node with the same technologies, the STM32V8 brings with it improved performance, higher density, and better power efficiency.

Industrial Applications

This new microcontroller is a multipurpose system to benefit several industries:

• Factory Automation and Robotics
• Audio Applications
• Smart Cities and Buildings
• Energy Management Systems
• Healthcare and Biosensing
• Transportation (ebikes)

Achievements

ST’s new microcontroller has been selected by SpaceX for its high-speed connectivity system in the Starlink Satellite System.

“The successful deployment of the Starlink mini laser system in space, which uses ST’s STM32V8 microcontroller, marks a significant milestone in advancing high-speed connectivity across the Starlink network. The STM32V8’s high computing performance and integration of large embedded memory and digital features were critical in meeting our demanding real-time processing requirements, while providing a higher level of reliability and robustness to the Low Earth Orbit environment, thanks to the 18nm FD-SOI technology. We look forward to integrating the STM32V8 into other products and leveraging its capabilities for next-generation advanced applications,” said Michael Nicolls, Vice President, Starlink Engineering at SpaceX.

STM32V8, like its predecessors, is expected to draw significant benefit from ST’s edge AI ecosystem, which is under continued expansion. Currently, the STM32V8 is in early-stage access for selected customers with key OEMs’ availability as of the first quarter 2026 and with broader availability to follow.

Apart from unveiling the new generation microcontroller, ST also announced the expansion of its STM32 AI Model Zoo, which is part of the comprehensive ST Edge AI Suite of tools. The STM32 AI Model Zoo has more than 140 models from 60 model families for vision, audio, and sensing AI applications at the edge, making it the largest MCU-optimised library of its kind.

This AI Model Zoo has been designed, keeping in mind the requirements of both data scientists and embedded systems engineers, a model that’s accurate enough to be useful and that also fits within their energy and memory constraints.

The STM32 AI Model Zoo is the richest in the industry, for it not only offers multiple models, but also scripts to easily retrain models, evaluate accuracy, and deploy on boards. ST has also introduced native support for PyTorch models. This complements their existing support for TensorFlow, Keras AI frameworks, LiteRT, and ONNX formats, giving developers additional flexibility in their development workflow. They are also introducing more than 30 new families of models, which can use the same deployment pipeline. Many of these models have already been quantised and pruned, meaning that they offer significant memory size and inference time optimisations while preserving accuracy.

Additionally, they also announced the release of STM32 Sidekick, their new AI agent on the ST Community, available 24/7. This new AI agent is trained on official STM32 documentation (datasheets, reference manuals, user manuals, application notes, wiki entries, and community knowledge base articles) to help users locate relevant technical data, obtain concise summaries of complex topics, and discover insights and documents. Alongside, they announced STM32WL3R, a version of their STM32WL3 tailored for remote control applications supporting the 315 MHz band. The STM32WL3R is a sub-GHz wireless microcontroller with an ultra-low-power radio.

~ Shreya Bansal, Sub-Editor

The post Redefining Edge Computing: How the STM32V8 18nm Node Outperforms Legacy 40nm MCUs appeared first on ELE Times.

Vitrealab GmbH of Vienna, Austria, a developer of photonic integrated circuits (PICs) for laser–LCoS-based augmented reality (AR) light engines, has closed a significantly oversubscribed $11m Series A financing round, led by LIFTT Italian Venture Capital and LIFTT EuroInvest with participation from Constructor Capital, aws Gründungsfonds, Gateway Ventures, PhotonVentures, xista Science Ventures, Moveon Technologies, and Hermann Hauser Investment...

🎓 Зимовий вступ 2026 у КПІ: нульовий курс «Відкритий шлях до вищої освіти» kpi ср, 01/07/2026 - 12:00

Union Electronics and IT Minister Ashwini Vaishnaw predicted that ‘Bharat’ will become a major player in the entire electronics stack, in terms of design, manufacturing, operating system, applications, materials, and equipment.

In an X post, the Union Minister drew attention to a major milestone for Prime Minister Narendra Modi’s ‘Make in India’ initiative and making India a major producer economy since Apple shipped $50 billion worth of mobile phones in 2025.

“Electronics production has increased six times in the last 11 years. And electronics exports have grown 8 times under PM Modi’s focused leadership. This progress has propelled electronics products among the top three exported items,” Vaishnaw noted.

He further informed that 46 component manufacturing projects, laptop, server, and hearable manufacturers had added to the ecosystem, which are making electronics manufacturing a major driver of the manufacturing economy.

“Four semiconductor plants will start commercial production this year. Total jobs in electronics manufacturing are now 25 lakh, with many factories employing more than 5,000 employees in a single location. Some plants employ as many as 40,000 employees in a single location,” the minister informed, adding that “this is just the beginning”.

Last week, the industry welcomed the approval of 22 new proposals under the third tranche of the Electronics Components Manufacturing Scheme (ECMS) by the government, saying that it marks a decisive inflexion point in India’s journey towards deep manufacturing and the creation of globally competitive Indian champions in electronics components.

With this, the total number of ECMS-approved projects rises to 46, taking cumulative approved investments to over Rs 54,500 crore. Earlier tranches saw seven projects worth Rs 5,532 crore approved on October 22 and 17 projects amounting to Rs 7,172 crore on November 17. The rapid scale-up across tranches underscores the strong industry response and the growing confidence in India’s components manufacturing vision.

According to the IT Ministry, the 22 projects approved in the third tranche are expected to generate production worth Rs 2,58,152 crore and create 33,791 direct jobs.

The post “‘Bharat’ will become a major player in entire electronics stack…”, Predicts Union Minister, Ashwini Vaishnaw appeared first on ELE Times.

EDOM Technology announced the introduction of the NVIDIA Jetson T4000 edge AI module, addressing the growing demand from system integrators, equipment manufacturers, and enterprise customers for balanced performance, power efficiency, and deployment flexibility. With powerful inference capability and a lightweight design, NVIDIA Jetson T4000 enables faster implementation of practical physical AI applications.

Powered by NVIDIA Blackwell architecture, NVIDIA Jetson T4000 supports Transformer Engine and Multi-Instance GPU (MIG) technologies. The module integrates a 12-core Arm Neoverse-V3AE CPU, three 25GbE network interfaces, and a wide range of I/O options, making it well-suited for low-latency, multi-sensor, and real-time computing requirements. In addition, Jetson T4000 features a third-generation programmable vision accelerator (PVA), dual encoders and decoders, and an optical flow accelerator. These dedicated hardware engines allow stable AI inference even under constrained compute and power budgets, making the platform particularly suitable for mid-range models and real-time edge applications.

For system integrators (SIs), the modular architecture of Jetson T4000, combined with NVIDIA’s mature software ecosystem, enables rapid integration of vision, sensing, and control systems. This significantly shortens development and validation cycles while improving project delivery efficiency, especially for multi-site and scalable edge AI deployments.

For equipment manufacturers, Jetson T4000’s compact form factor and low-power design allow flexible integration into a wide range of end devices, including advanced robotics, industrial equipment, smart terminals, machine vision systems, and edge controllers. These capabilities help manufacturers bring stable AI inference into products with limited space and power budgets, accelerating intelligent product upgrades.

Enterprise users can deploy Jetson T4000 across diverse scenarios such as smart factories, smart retail, security, and edge sensor data processing. By performing inference and data pre-processing at the edge, organisations can reduce system latency, lower cloud workloads, and improve overall operational efficiency—while maintaining system stability and deployment flexibility.

In robotics and automation applications, Jetson T4000 features low power consumption, high-speed I/O and a compact footprint, making it an ideal platform for small mobile robots, educational robots, and autonomous inspection systems, delivering efficient and reliable AI computing for a wide range of automation use cases.

NVIDIA Jetson product lineup spans from lightweight to high-performance modules, including Jetson T4000 and T5000, addressing diverse requirements ranging from compact edge devices and industrial control systems to higher-performance inference applications. With NVIDIA’s comprehensive AI development tools and SDKs, developers can rapidly port models, optimise inference performance, and seamlessly integrate AI capabilities into existing system architectures.

Beyond supplying Jetson T4000 modules, EDOM Technology leverages its extensive ecosystem of partners across chips, modules, system integration, and application development. Based on the specific development stages and requirements of system integrators, equipment manufacturers, and enterprise customers, EDOM provides end-to-end support—from early-stage planning and technical consulting to ecosystem enablement. By sharing ecosystem expertise and practical experience, EDOM helps both existing customers and new entrants to the edge AI domain quickly build application capabilities and deploy edge AI solutions tailored to real-world scenarios.

The post NVIDIA’s Jetson T4000 for Lightweight & Stable Edge AI Unveiled by EDOM appeared first on ELE Times.

Anritsu Corporation will exhibit Battery Cycler and Emulation Test System RZ-X2-100K-HG, planned for sale in the North American market as an evaluation solution for eMobility, at CES 2026 (Consumer Electronics Show), one of the world’s largest technology exhibitions to be held in Las Vegas, USA, from January 6 to January 9, 2026.

The launch of the RZ-X2-100K-HG in the North American market represents the first step in the global expansion efforts of TAKASAGO, LTD., which holds a significant share in the domestic EV development market, and it is an important measure looking ahead to future global market growth.

At CES 2026, a concept exhibition will showcase the Power HIL evaluation system combining the RZ-X2-100K-HG with dSPACE’s HIL simulator, demonstrating a new direction for the EV evaluation process.

Additionally, the power measurement solutions from DEWETRON, which joined the Anritsu Group in October 2025, will also be exhibited. Using a three-phase motor performance evaluation demonstration, we will present example applications.

About the RZ-X2-100K-HG

The RZ-X2-100K-HG is a test system developed by TAKASAGO, LTD. of the Anritsu Group, equipped with functions for charge-discharge testing and battery emulation that support high voltage and large current. It is a model based on the RZ-X2-100K-H, which has a proven track record in Japan, adapted to comply with the United States safety standards and input power specifications. This system is expected to be used for testing the performance, durability, and safety of automotive batteries and powertrain devices in North America.

About Power HIL

Power HIL (Power Hardware-in-the-Loop) is an extended simulation technology that combines virtual and real elements by adding a “real power supply function” to HIL (Hardware-in-the-Loop). Power HIL creates a virtual vehicle environment with real power, reproducing EV driving tests and charging tests compatible with multiple charging standards under conditions close to reality. This allows for high-precision and efficient evaluation of battery performance, safety, and charging compatibility without using an actual vehicle.

Terminology Explanation

[*] Battery Emulation Test System

A technology that simulates the behaviour of real batteries (voltage, current, internal resistance, etc.) using a power supply device to evaluate how in-vehicle equipment operates.

The post Anritsu to Bring the Future of Electrification Testing at CES 2026 appeared first on ELE Times.

Keysight Technologies, Inc. introduced Keysight AI Software Integrity Builder, a new software solution designed to transform how AI-enabled systems are validated and maintained to ensure trustworthiness. As regulatory scrutiny increases and AI development becomes increasingly complex, the solution delivers transparent, adaptable, and data-driven AI assurance for safety-critical environments such as automotive.

AI systems operate as complex, dynamic entities, yet their internal decision processes often remain opaque. This lack of transparency creates significant challenges for industries, such as automotive, that must demonstrate safety, reliability, and regulatory compliance. Developers struggle to diagnose dataset or model limitations, while emerging standards — such as ISO/PAS 8800 for automotive and the EU AI Act- mandate explainability and validation without prescribing clear methods. Fragmented toolchains further complicate engineering workflows and heighten the risk of conformance gaps.

Keysight AI Software Integrity Builder introduces a unified, lifecycle-based framework that answers the critical question: “What is happening inside the AI system, and how do I ensure it behaves safely in deployment?” The solution equips engineering teams with the evidence needed for regulatory conformance and enables continuous improvement of AI models. Unlike fragmented toolchains that address isolated aspects of AI testing, Keysight’s integrated approach spans dataset analysis, model validation, real-world inference testing, and continuous monitoring.

Core capabilities of Keysight AI Software Integrity Builder include:

• Dataset Analysis: Analyse data quality using statistical methods to uncover biases, gaps, and inconsistencies that may affect model performance.
• Model-Based Validation: Explains model decisions and uncovers hidden correlations, enabling developers to understand the patterns and limitations of an AI system.
• Inference-Based Testing: Evaluates how models behave under real-world conditions, detects deviations from training behaviour, and recommends improvements for future iterations.

While open-source tools and vendor solutions typically address only isolated aspects of AI testing, Keysight closes the gap between training and deployment. The solution not only validates what a model has learned, but also how it performs in operational scenarios — an essential requirement for high-risk applications such as autonomous driving.

Thomas Goetzl, Vice President and General Manager of Keysight’s Automotive & Energy Solutions, said: “AI assurance and functional safety of AI in vehicles are becoming critical challenges. Standards and regulatory frameworks define the objectives, but not the path to achieving a reliable and trustworthy AI deployment. By combining our deep expertise in test and measurement with advanced AI validation capabilities, Keysight provides customers with the tools to build trustworthy AI systems backed by safety evidence and aligned with regulatory requirements.”

With AI Software Integrity Builder, Keysight empowers engineering teams to move from fragmented testing to a unified AI assurance strategy, enabling them to deploy AI systems that are not only performant but also transparent, auditable, and compliant by design.

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