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STMicroelectronics Introduces STM32G5 MCUs for Power and Motor Control

By
Joydeep Kar
-
Image: STMicroelectronics
Image: STMicroelectronics

GENEVA, Switzerland, Nov 5, 2026 – STMicroelectronics (ST) has introduced the STM32G5 of mixed-signal microcontrollers (MCUs), featuring an Arm Cortex-M33 processor running at 280 MHz. The MCUs deliver twice the computing performance of the STM32G4 series and combine processing, analog control and hardware security features for digital power systems, motor control, robotics and industrial applications.

The STM32G5 series includes upgraded peripherals for controlling power supplies and electric motors. Applications include AI data centers, photovoltaic installations, wind turbines, industrial drives and commercial drones. ST also plans to expand the single-core portfolio with higher-performance dual-core devices offering additional peripherals in 2027.

Patrick Aidoune, general manager, general purpose and automotive microcontrollers division, STMicroelectronics, said: “This new generation of microcontrollers addresses the challenge to meet the growing energy needs of AI datacenters efficiently, realize more sophisticated humanoid robots, and power more complex motor-control systems. With the STM32G5 series, ST makes the compute performance needed for physical AI accessible and embeddable to deliver improvements that enhance sustainability, safety, and security.”

Digital Power and Motor Control

ST describes the STM32G5 as its first MCU with a 12-channel high-resolution timer. The series also includes motor-control timers, a position encoder and a programmable logic array (PLAy). These peripherals support precise control of power supplies and electric motors.

The timer supports control algorithms that adjust power supply operation as server workloads change. The MCUs also support higher switching frequencies in power supplies using silicon carbide (SiC) and gallium nitride (GaN) semiconductors.

“Digital power is at the heart of our mission to deliver innovative, clean, and energy-efficient solutions for a better tomorrow, and it requires control platforms that can keep pace with increasingly complex systems. The STM32G5 brings the level of performance and flexibility needed for next-generation digital-power applications. The combination of processing power, integrated peripherals, and built-in security makes it a strong option for our designs, helping deliver lower energy losses, a smaller footprint, and higher reliability in demanding AI and EV applications,” said Andy Hsiao, senior commodity buyer, PSBG materials division, Delta Electronics.

“In humanoid robotics, dexterity depends on combining strength, precision, sensing and real-time control in an extremely compact space,” said Paul Tian, Ruiyan Intelligent CEO. “The STM32G5 delivers more than twice the performance of the previous generation, more peripheral support and stronger built-in security needed to control more motors, respond faster when speed and precision matter, and integrate more sensors into the robotic hand. Together with ST, we’re bringing dexterous robotic hands closer to real-world use, from factory automation to medical laboratories, with the goal of making everyday life easier.”

Hardware Security and Processing Features

The PLAy peripheral allows developers to implement custom functions in on-chip hardware, reducing the need for external logic components.

Security features combine Arm TrustZone with ST’s STM32Trust technologies, including an immutable Root of Trust (STiRoT) for secure boot and firmware updates. The devices also provide a cryptographic engine supporting algorithms covered by ST’s post-quantum cryptography (PQC) libraries, software protection, secure storage for cryptographic keys, tamper detection, side-channel attack resistance and life cycle security management.

Additional hardware specifications include:

  • Cortex-M33 processor delivering 420 DMIPS and 1,136 CoreMark at 280 MHz.
  • Instruction and data caches and a routine booster for processing acceleration.
  • A 12-bit analog-to-digital converter (ADC), operational amplifier, 12-bit digital-to-analog converter (DAC) and ultra-fast comparators.
  • Motor-control timers and a position encoder (POSENC).
  • Low-profile quad flat package (LQFP) options in two variants, including a thermally enhanced exposed-pad version required for data center power applications.

Development Tools and Software

ST supports the STM32G5 series with hardware development boards, software libraries, documentation and training resources for digital power and motor-control applications.

The devices are supported by STM32CubeMX2, which provides live code previews, graphical configuration tools, clock and pinout configuration features, and online documentation. The new STM32Cube hardware abstraction layer (HAL2) provides access to STM32G5 hardware features with an optimized memory footprint.

Development resources include:

  • NUCLEO-G564RE: STM32 Nucleo board with an STM32G5-512K device in an LQFP64 package.
  • B-G564E-MC1S: Motor Control Discovery Kit.
  • B-G564E-DPOW1: Digital Power Discovery Kit.
  • X-CUBE-DPOWER: Digital power software libraries.
  • X-CUBE-MCSDK: Motor-control software development kit.

The STM32G5 series is now in production, with performance and product lines offering 64 KB to 512 KB of on-chip flash memory.

Source: STMicroelectronics

About STMicroelectronics

STMicroelectronics (ST) is a semiconductor company founded in 1987. It designs and manufactures microcontrollers, sensors, power devices and other analog and mixed-signal components. The company operates as an integrated device manufacturer with in-house production that supports large-volume supply for global markets. Its customers include automotive, industrial, consumer electronics and communications equipment manufacturers. ST also works with technology partners to develop components used in mobility systems, energy and power management equipment and connected devices. The company reports ongoing efforts to reduce emissions across its operations and increase the share of renewable electricity in its manufacturing sites, with a goal to reach full renewable sourcing by 2027. ST is headquartered in Geneva, Switzerland. It serves more than 200,000 customers worldwide and employs about 50,000 people. Its technologies support applications that require efficient power conversion, sensing, processing and connectivity. The company expands its portfolio to support automotive, industrial, consumer and connected system applications through integrated design and manufacturing.

AI Disrupts CAD as Engineering Software Faces an Uncertain Future

By
Ruchika Saini, AI
-
Created with AI using Google’s Nano Banana 2K (source: Machine Design).
Created with AI using Google’s Nano Banana 2K (source: Machine Design).

 

Artificial intelligence (AI) is creating opportunities and uncertainties for the mechanical computer-aided design (MCAD) industry. In this Machine Design article, CAD industry analyst Ralph Grabowski examines whether AI can transform engineering software while challenging established subscription models and traditional design workflows.

Grabowski argues that CAD vendors have struggled to deliver the benefits promised by cloud computing. Many engineers continue using desktop applications such as SolidWorks and Autodesk Inventor because of their functionality, compatibility, and reliability. Migrating established systems to the cloud remains expensive and technically difficult.

AI introduces another disruption. Tools capable of generating software through natural-language instructions could enable smaller developers to compete with established CAD vendors. However, Grabowski questions whether current AI capabilities justify industry expectations.

A major concern is reliability. Large language models (LLMs) produce probabilistic outputs, making them unsuitable for engineering tasks requiring predictable geometric accuracy. In CAD Bench v3 testing, Claude Opus 5.5 reportedly completed 61% of complex modeling tasks successfully, illustrating the limitations of current AI-assisted CAD.

The article also examines emerging alternatives. Microsoft is researching AI-driven modeling technologies, including FlexCAD, CADFusion, CAD-Editor, and CADMorph. Meanwhile, KittyCAD’s Zoo Design Studio converts natural-language requests into parametric modeling code.

Grabowski suggests that future CAD systems could move beyond command-based interfaces toward goal-oriented engineering agents. These agents would interpret design requirements, identify relevant components, execute modeling operations, evaluate alternatives, and coordinate simulations.

However, intellectual property protection remains a concern when proprietary engineering information is shared with external AI providers.

The author also highlights physics-aware world models as a promising alternative to conventional LLMs. Unlike systems relying primarily on statistical predictions, these approaches could incorporate physical constraints and engineering simulation.

Grabowski concludes that AI may improve CAD productivity, but deterministic modeling, reliable simulation, and experienced engineers will remain essential to mechanical design.

Thermal Design Becomes Critical as Cars Gain Computing Power

By
Ruchika Saini, AI
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Thermal performance should be specified in the early stage of vehicle architecture rather than optimized at the end (source: sefa ozel/E+ via Getty Images).
Thermal performance should be specified in the early stage of vehicle architecture rather than optimized at the end (source: sefa ozel/E+ via Getty Images).

 

As modern vehicles incorporate increasingly powerful computing systems, thermal management is becoming a fundamental engineering challenge. This Design News article explains that advanced driver assistance systems (ADAS), centralized controllers, and high-voltage power electronics are generating greater heat loads, requiring engineers to consider thermal performance from the earliest stages of vehicle design.

Traditional automotive architectures often distribute functions across more than 100 electronic control units (ECUs). Newer designs consolidate these functions into fewer, more powerful processors, increasing computing density and concentrating heat within limited spaces.

The article highlights research from the U.S. Department of Energy (DOE) and the National Renewable Energy Laboratory (NREL), which explores power electronics capable of reaching 100 kilowatts per liter. One research concept involving a 100-kW wide-bandgap inverter estimates a heat load of 2,150 watts and a maximum device temperature of 250°C.

Wide-bandgap semiconductors, including silicon carbide (SiC) and gallium nitride (GaN), offer higher switching speeds and improved efficiency. However, their capabilities introduce additional challenges involving packaging, heat extraction, material compatibility, and long-term reliability.

Thermal engineering must therefore address the entire vehicle system, including semiconductor junctions, circuit boards, cooling channels, pumps, and heat exchangers. Engineers must also account for temporary temperature spikes caused by changing processor workloads and electrical operating conditions.

Simulation tools, including computational fluid dynamics, circuit simulation, and reduced-order modeling, allow engineers to evaluate thermal behavior before building physical prototypes. Software-based workload management can also help regulate heat generation by adjusting computational activity.

Cooling systems introduce their own energy demands, requiring engineers to balance thermal performance against vehicle efficiency, weight, and packaging constraints. Integrated coolant loops may reduce duplicated hardware but increase interactions between subsystems.

Ultimately, the article emphasizes that effective automotive thermal design requires coordinated decisions across electronics, mechanical engineering, cooling hardware, and software.

AI-Powered DFM Tools Bring Manufacturing Feedback Into Early Design

By
Ruchika Saini, AI
-
Source: Leo AI.
Source: Leo AI.

 

Design for manufacturing (DFM) analysis is evolving as artificial intelligence (AI) enables engineers to identify manufacturability problems earlier in product development. According to Leo AI, traditional DFM reviews often occur too late, forcing teams to revise designs after significant engineering work has already been completed.

The article examines four categories of DFM tools available in 2026: manual checklists, CAD-integrated analysis, supplier-side platforms, and AI-powered engineering knowledge retrieval.

CAD-integrated tools, including DFMPro, SolidWorks DFMXpress, and DFMA, automatically identify potential manufacturing problems such as thin walls, sharp corners, and deep pockets. Supplier platforms such as Protolabs, Xometry, and Hubs provide manufacturability feedback during quotation, helping engineers evaluate production feasibility.

However, manufacturing requirements depend on materials, processes, equipment, and supplier capabilities. A feature suitable for sheet metal fabrication may be unsuitable for injection molding. Consequently, generic design rules cannot address every manufacturing constraint.

AI introduces another capability by connecting design decisions with previously documented engineering knowledge. Information from engineering change orders, supplier quality reports, manufacturing records, and historical design reviews can help engineers identify problems that conventional geometric checks might overlook.

Leo AI illustrates this approach through its integration with product data management (PDM) and product lifecycle management (PLM) systems, including SolidWorks PDM, Autodesk Vault, PTC Windchill, and Siemens Teamcenter. Engineers can search technical documentation and manufacturing history using natural language to retrieve relevant guidance.

The article recommends combining AI-based research during conceptual design, CAD-integrated checks during detailed development, and supplier validation before manufacturing release.

It also emphasizes documenting lessons from supplier feedback so organizations can reuse manufacturing knowledge across future projects.

Ultimately, effective DFM analysis requires more than automated rule checking. Combining engineering expertise, manufacturing data, and AI-assisted knowledge retrieval can reduce late-stage modifications, improve collaboration, and support more manufacturable designs.

3D Systems Reports Q3 Growth, Momentum Across 3D Printing Markets

By
Joydeep Kar
-

ROCK HILL, SC, Nov 5, 2026 – 3D Systems reported Q3 FY26 results, reporting $99M in revenue – a 5% increase from $95M in Q2 FY26 – with a loss of $34M.

Third Quarter Financial Highlights

• Total revenue reached $99.3M, supported by growth in healthcare and industrial solutions.
• Healthcare Solutions revenue increased approximately 14.4% to $49 million compared to the prior-year period.
• Operating expenses increased to $59.3 million from $50.7 million in the prior-year period, reflecting higher selling, general and administrative expenses and asset impairment charges.
• Cash and cash equivalents totaled $91.3 million.
• Industrial Solutions revenue increased approximately 3.9% to $50.3 million compared to the prior-year period.
• Aerospace & Defense revenue grew more than 50% year over year, while Data Center Infrastructure also recorded strong growth.
• Healthcare parts manufacturing and dental materials sales each increased more than 20% year over year.
• Net loss widened to $33.5 million from $18.1 million in the prior-year period, partly reflecting litigation expenses and asset impairment charges.
• Adjusted EBITDA improved to a profit of $3.1 million from a $10 million loss in the prior-year period.

Eight-quarter financial trends: A comparative analysis of 3D Systems’ results.
Eight-quarter financial trends: A comparative analysis of 3D Systems’ results.

Dr. Jeffrey Graves, president and chief executive officer of 3D Systems, said, “We are pleased with our third-quarter and year-to-date performance. Revenue growth accelerated, Adjusted EBITDA turned positive for the quarter and the year-to-date period, and our four strategic markets, Med Tech, Dental, Aerospace & Defense, and Data Center Infrastructure, continued to deliver strong double-digit growth. These results reflect the progress we have made executing a focused strategy: concentrating resources on high-value production applications, refreshing our product portfolio, and expanding our metal parts manufacturing capability. Customers in these markets are increasingly adopting 3D printing as a core manufacturing technology, and the combination of design flexibility and production economics is enabling higher-performance components and systems to move into full production.”

“Our positive third-quarter results demonstrate continued progress against the priorities we established over the past two years,” said Phyllis Nordstrom, chief financial officer of 3D Systems. “We delivered strong revenue growth while maintaining effective cost management, resulting in positive Adjusted EBITDA year-to-date. At the same time, we are steadily reshaping our business around our four strategic markets through the first three quarters of the year. Our year-to-date performance gives us confidence that our strategy is gaining traction and creating a stronger foundation for profitable growth.”

A detailed chart outlining the financial results is available here, providing a comprehensive breakdown of key metrics and performance indicators for better insight into the company’s financial standing.

Source: 3D Systems

Source: 3D Systems

3D Systems was founded in 1986 by Charles “Chuck” Hull, the inventor of stereolithography (SLA) and the first commercial 3D printer, the SLA-1. Based in Rock Hill, SC, the company has played a central role in the development of additive manufacturing technologies. Today, 3D Systems provides a range of 3D printing solutions that include hardware, software, and materials. Its offerings support a wide variety of industries – such as aerospace, automotive, healthcare, dental, and consumer goods – enabling workflows that range from early-stage prototyping to full-scale production. Its infrastructure spans offices, manufacturing facilities, and Customer Innovation Centers designed to support integration of 3D printing into industrial and commercial operations. Over the decades, 3D Systems has evolved from a technology originator into a solutions provider, focusing on application-specific capabilities across sectors including medical and dental, aerospace and defense, transportation, AI infrastructure, and industrial goods. Its engineering teams focus on improving manufacturing outcomes using additive technologies.

Mach9 Expands AI-Powered CAD to Aerial LiDAR and Photogrammetry

By
Ruchika Saini, AI
-
Source: AEC Magazine.
Source: AEC Magazine.

 

Mach9 has expanded its artificial intelligence (AI)-powered CAD platform to support aerial LiDAR and photogrammetry datasets collected by drones, helicopters, and aircraft. Previously focused on ground-based scanning, the web-based software now helps civil engineering and geospatial teams convert aerial survey data into engineering-ready drawings and terrain models, tells AEC Magazine.

The expansion addresses a persistent challenge in reality capture: collecting large volumes of survey data is considerably faster than processing them into usable engineering deliverables. Mach9 combines automated feature extraction, point cloud classification, CAD editing, and surface modeling within a single application.

Surveyors can upload aerial point clouds and orthomosaic imagery, allowing AI algorithms to generate initial drawings within hours. The software automatically identifies curbs, building footprints, pavement edges, and vegetation boundaries, producing three-dimensional points and lines with claimed centimeter-level accuracy.

For electrical and telecommunications infrastructure, Mach9 can extract utility poles and suspended wire geometries. These capabilities support clearance analysis, utility planning, and pole-loading assessments.

The platform also generates ground surfaces, breaklines, and contours for land development, solar farms, data centers, and infrastructure projects. Engineers can manually refine classifications, modify surface vertices, and use deviation heatmaps to identify areas requiring correction.

Early adopters report measurable productivity improvements. Engineering consultancy Langan used Mach9 on projects covering several thousand acres in Texas, reducing delivery times by approximately 40%. Welch Comer reported average savings of four to five hours per project.

Mach9 supports aerial mapping equipment from manufacturers including Leica, Trimble, RIEGL, and DJI. Export formats include DXF, DWG, SHP, GDB, and LandXML, enabling integration with existing CAD and geographic information systems.

By combining AI automation with human review, Mach9 aims to reduce manual processing, accelerate survey deliverables, and help engineering firms manage larger infrastructure projects without increasing their reliance on multiple specialized software applications.

Eight Ways PLM Software Can Stay Competitive in the AI Era

By
Ruchika Saini, AI
-
Source: Beyond PLM.
Source: Beyond PLM.

 

Artificial intelligence (AI) is changing the economics of product lifecycle management (PLM) software by making applications easier to develop, customize, and reproduce. However, Beyond PLM author Oleg Shilovitsky argues that established PLM vendors can remain competitive by focusing on capabilities that extend beyond software functionality.

The article identifies eight competitive advantages, or moats, that could help PLM providers maintain their relevance as AI-generated applications become increasingly accessible.

The first is industry trust, built through reliable support, implementation experience, and professional communities. The second involves authoritative product records and Product Memory, which preserve engineering decisions, revisions, configurations, and relationships throughout a product’s lifecycle.

Third, AI agents capable of executing approved engineering workflows could strengthen customer relationships by handling tasks such as change management, document distribution, and supplier coordination.

Fourth, dependable bill of materials (BOM) calculations remain valuable because manufacturers require accurate quantities, costs, configurations, and traceable assumptions.

The fifth advantage is regulatory compliance, where validated processes, audit records, and documented approvals create barriers to replacing established systems.

Sixth, collaboration networks connecting original equipment manufacturers, suppliers, and contractors become increasingly valuable as participants depend on shared engineering information.

Seventh, PLM platforms can learn from manufacturing outcomes, supplier feedback, and engineering changes to improve future decisions. Finally, continuously maintained BOM and service information provides value that AI-generated interfaces alone cannot reproduce.

Shilovitsky also examines changing business models. Instead of charging primarily for software access, vendors could increasingly provide completed engineering services, such as preparing supplier packages, coordinating approvals, and tracking responses.

Although AI may reduce software development costs, manufacturers will continue requiring dependable product information, traceability, and accountable decisions.

The article concludes that PLM’s long-term value will depend less on proprietary features and more on trusted information, operational responsibility, collaboration, and accumulated engineering knowledge.

AI Transforms Space Operations Through Smarter Mission Decisions

By
Ruchika Saini, AI
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Ground-based monitoring systems track satellite positions and potential close approaches, providing the situational awareness needed for safe space operations (source: Ansys).
Ground-based monitoring systems track satellite positions and potential close approaches, providing the situational awareness needed for safe space operations (source: Ansys).

 

As satellite constellations expand and space missions become increasingly complex, operators face growing challenges in managing enormous volumes of information. Ansys, part of Synopsys, highlights how artificial intelligence (AI) can help mission teams analyze operational data, identify problems, and make faster decisions without replacing human expertise.

With more than 11,000 active satellites generating telemetry, observations, and intelligence, space operators must continuously monitor spacecraft health, coordinate sensors, plan maneuvers, and respond to changing mission requirements. AI offers opportunities to manage this information more efficiently.

One application is spacecraft anomaly detection. Rather than simply identifying unusual behavior, AI can analyze multiple indicators, recognize patterns, and suggest probable causes. This capability could reduce troubleshooting time and help operators prioritize critical issues.

AI-powered agents could also automate repetitive activities, including reviewing overnight operations, checking spacecraft health, scheduling resources, and generating reports. Instead of manually gathering information, operators could receive AI-generated briefings summarizing important developments and recommending actions.

Another opportunity involves conversational interfaces for mission engineering tools. Operators could request alternative maneuver plans, evaluate observation opportunities, or generate training scenarios using natural language. Simulation systems would perform the underlying analysis and present results for review.

AI could also improve mission readiness by generating realistic training exercises involving communications failures, spacecraft anomalies, or satellite coordination challenges.

However, Ansys emphasizes that AI requires a reliable engineering foundation. Digital mission engineering connects mission models, digital twins, simulation environments, and operational workflows, enabling teams to evaluate AI-assisted recommendations before implementing them.

By combining AI with physics-based simulation and mission context, space organizations can improve situational awareness, reduce operational workloads, and make informed decisions while maintaining human oversight.

Cadence ChipStack AI Agent Automates Chip Design and Verification

By
Ruchika Saini, AI
-
Source: Cadence.
Source: Cadence.

 

Cadence Design Systems is advancing semiconductor development with its ChipStack AI Super Agent, an agentic artificial intelligence (AI) system designed to coordinate front-end chip design and verification. Rather than automating isolated tasks, the technology connects engineering activities through a shared understanding of design requirements, implementation details, and verification results.

ChipStack uses a design-specific mental model built from specifications, register-transfer level (RTL) code, testbenches, block diagrams, and behavioral models. This model captures design intent, functionality, hierarchy, and relationships, helping specialized AI agents identify inconsistencies and coordinate subsequent engineering activities.

Engineers can provide a high-level objective, such as verifying RTL against specifications, correcting errors, and generating coverage reports. ChipStack interprets the request, develops verification plans, creates test environments, executes simulations, analyzes failures, and proposes corrections.

The system integrates Cadence technologies, including Xcelium Logic Simulation, Jasper Formal Verification, Verisium Manager, and Verisium Debug. These tools provide simulation results, formal verification evidence, failure analysis, and coverage metrics that support engineering decisions.

For example, ChipStack can analyze regression failures, group related issues, identify root causes, modify RTL or testbenches, and repeat verification until the required objectives are addressed.

Cadence reports productivity improvements of up to 10 times across coding, testbench development, regression testing, and debugging. In one demonstrated intellectual property verification workflow, RTL validation accelerated by more than 40 times, reducing a five-week process to less than one day.

ChipStack also supports Level-5 autonomy, allowing agents to independently determine subsequent actions and coordinate complex workflows. Engineers can review intermediate results, adjust objectives, and approve final outcomes.

The technology supports customer-defined engineering practices and cloud-based or on-premises AI models. Its central objective is to reduce manual coordination while preserving design intent, verification evidence, and human accountability throughout semiconductor development.

New VR Technology Converts Touch Into Visual Cues for Better Collaboration

By
Ruchika Saini, AI
-
Sensory asymmetry between collaborators in virtual reality (source: KAIST).
Sensory asymmetry between collaborators in virtual reality (source: KAIST).

 

Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed a virtual reality (VR) technique that transforms tactile information into visual cues, allowing users to recognize surface roughness without specialized haptic equipment. The approach addresses a common challenge in collaborative VR environments, where participants often have access to different sensory capabilities, tells Tech Xplore.

In conventional VR systems, haptic devices provide tactile feedback through vibrations, enabling users to experience virtual textures. However, participants without these devices must rely entirely on visual information, creating an imbalance that can complicate collaborative tasks.

To overcome this limitation, researchers from KAIST’s Ubiquitous Virtual Reality Lab, led by Professor Woontack Woo, developed a method that represents surface roughness through changes in a virtual hand’s outline. By modifying the outline’s shape and movement, the system communicates three levels of roughness: low, medium, and high.

The researchers conducted preliminary experiments to verify that users could distinguish the visual cues and associate them with corresponding tactile sensations. They subsequently tested the approach with 48 participants divided into 24 pairs. Each pair included one participant equipped with a haptic device and another without one.

During collaborative object-sorting tasks, teams using visual roughness cues on both participants’ virtual hands completed their work approximately 23% faster than teams without the cues, while maintaining high accuracy. Participants lacking haptic equipment also reported greater confidence in their decisions and a stronger sense of contribution.

The research demonstrates that visual representations of touch can reduce sensory differences between VR users without requiring identical hardware.

Potential applications include VR-based education, technical training, and remote collaboration, where participants may use different equipment.

The study received the Best Paper Award at the IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2026), recognizing its contribution to collaborative immersive technologies.

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