PLM news, announcements and industry trends.

Beyond PLM · Oct 15, 2026

PLM Software

AI-generated summary: PLM adoption problems reflect software architecture as well as organizational structures, argues Beyond PLM author Oleg. Responding to Jos Voskuil’s discussion of Conway’s Law, he accepts that departmental incentives can impede collaboration and that PLM is a strategy supported by federated systems, not one application for every discipline. However, Conway’s Law—which links system structure to an organization’s communication structure—does not establish that existing PLM approaches are usable or that reorganization is the only remedy. Voskuil’s clarification that organizational change and software can develop together frames the article’s central question: how can architecture help companies move from existing practices toward better collaboration? The author distinguishes technical feasibility from practical adoption. A PDM–ERP integration may be possible yet difficult to implement within available budgets and skills, preserve identities and revisions, diagnose failures, or maintain as connected systems change. Similarly, requiring an ERP part number before engineering can proceed may reflect either a legitimate business requirement or software demanding production-ready information prematurely. Adoption should therefore be designed into the architecture, with useful early outcomes and benefits for contributors—not extra engineering administration whose rewards accrue only downstream. Training cannot indefinitely compensate for repeated entry and unnecessary handoffs. The proposed alternative is a task-focused “seamless product context layer,” rather than a comprehensive single source of truth. It would assemble selected information from existing authoritative systems through integrations, imports, files, or references, while those systems retain their responsibilities. A sourcing review could connect BOM lines, quotations, inventory and spreadsheets; an engineering change could connect CAD revisions, configurations, tests and previous decisions. Initial integration would cover only the task’s requirements, expanding as value becomes evident. Nevertheless, the layer must preserve identity, version, configuration, provenance and acceptance status; graph relationships and semantic models could express these connections. Several vendor examples illustrate different architectural roles, not interchangeable products or demonstrated adoption outcomes. CoLab connects browser-based design reviews with Windchill and Teamcenter, tracking feedback and ownership and returning summaries or issues to PLM. PTC describes Jetstream as a cloud-native collaboration environment linking internal and external contributors with PLM, ALM and CAD; the author cautions that deployment experience remains to be assessed. Siemens describes Intelligence Center X as combining industrial ontologies, knowledge graphs, AI and workflow orchestration through Mendix, Graph Studio and AI Studio, with usefulness dependent on mappings and connections. Autodesk Platform Services’ Manufacturing Data Model APIs enable granular access to cloud design information without desktop authoring software, but developers must still build the surrounding context and application. OpenBOM’s Product Memory vision connects CAD/PDM, BOM, ERP, spreadsheet and supplier information through a Capture → Review → Flow cycle. Its BOM Review workflow combines deterministic checks on identifiers, required properties, quantities and structure with AI BOM Health observations such as near-duplicate parts or unusually long lead times. AI suggestions remain separate from validation errors, while findings can become comments and assigned tasks. The author, who discloses being OpenBOM’s co-founder and CEO, says his company must face the same test as other approaches: reducing review effort, preserving reasoning and supporting subsequent decisions. An illustrative build of twenty inspection-robot prototypes shows the approach in practice. A workspace would connect mechanical designs, controller and firmware references, the applicable BOM, quotations and available quantities. If purchasing proposes an unavailable controller’s replacement, contributors could assess enclosure fit, harness changes, tested firmware and schedule implications against the same intended build. The decision and supporting evidence would be retained, but accepted changes would still follow revision and publication controls: a new source version alone would not authorize procurement or establish compatibility. Over time, reviews, supplier discussions and test evidence could accumulate into Product Memory, including contributions delivered as spreadsheets, PDFs or STEP files. AI could extract supplier information, compare options and identify missing inputs, but should expose the evaluated product state, evidence and unresolved questions. Plausible answers can otherwise overlook configuration or qualification constraints. The author concludes that task-focused collaboration deserves testing, not unquestioned acceptance: identity resolution, access, mappings, record authority and maintenance remain substantial responsibilities. Leadership must still address incentives, while architecture should demonstrate lower total coordination costs and let practical results guide broader transformation.

Siemens Digital Industries Software · Oct 15, 2026

CAD, CAE & Simulation · Partnerships & Acquisitions

Siemens today announced significant advances in its collaboration with NVIDIA to accelerate computational lithography workflows. By integrating NVIDIA cuLitho , a CUDA-X library for GPU-accelerated computational lithography, with Calibre® manufacturing software, Siemens is enabling semiconductor manufacturers to overcome the runtime-limited challenges of advanced patterning, making next-generation AI semiconductors a manufacturing reality. The patterning bottleneck challenge As the industry aggressively moves toward leading-edge process nodes, the complexity of lithographic patterning has increased exponentially. Traditional optical proximity correction (OPC) and computational lithography techniques face a "runtime wall." The transition to High-NA EUV and the requirement for advanced curvilinear patterning - such as Inverse Lithography Technology (ILT) - demand massive computational resources. Without acceleration, these essential techniques are often too slow for full-chip, manufacturing-scale deployment, and forcing engineering teams to make trade-offs between optimization depth and project schedules. This creates a risk of lithographic hotspots and reduced process margins, which can impact yield for the most advanced AI compute and memory devices. GPU-accelerated patterning to radically accelerate lithography simulation Siemens and NVIDIA are transforming this challenge into an enabling technology. By delivering at least 10x acceleration for key lithography simulation workloads, the collaboration allows manufacturers to perform more comprehensive optimization and identify potential hotspots before tapeout. “Computational lithography has traditionally been one of the most compute-intensive stages of the design-to-manufacturing flow,” said Ankur Gupta, executive vice president, EDA IC Portfolio, Siemens. “By combining Siemens’ signoff-grade Calibre technology with NVIDIA’s GPU acceleration, we are giving our customers the ability to deploy advanced patterning strategies like curvilinear ILT at scale. This isn't just about speed; it's about enabling the precision required for the next generation of AI infrastructure.” “Next-generation chips are pushing the limits of manufacturing complexity, making computational lithography more critical than ever,” said Tim Costa, vice president and general manager of industrial and computational engineering at NVIDIA. “By bringing NVIDIA cuLitho acceleration to Siemens’ Calibre, we’re enabling engineers to tackle increasingly intricate patterns and explore more extensive optimizations needed to manufacture the world’s most advanced chips.” Measurable value to chip and photomask manufacturers The collaboration provides semiconductor and mask manufacturers with measurable performance gains across critical computational lithography workflows. Verified engineering data supports simulation speed-ups ranging from at least 10x to 17x for key workloads, helping make full-chip curvilinear patterning practical for High-NA EUV production. By reducing simulation runtimes, manufacturers can complete more iterations in less time, identify lithographic hotspots earlier and improve process margins before tapeout. This faster turnaround can also help accelerate manufacturing readiness and support the deployment of new AI silicon. To learn more about Siemens’ advanced semiconductor design and manufacturing solutions, please visit: https://blogs.sw.siemens.com/calibre/2026/10/14/the-virtuous-cycle-of-silicon-how-siemens-is-accelerating-the-era-of-physical-ai-with-nvidia-culitho/ Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens' software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today's ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation. Siemens Digital Industries (DI) empowers companies of all sizes within the process and discrete manufacturing industries to accelerate their digital and sustainability transformation across the entire value chain. Siemens’ cutting-edge automation and software portfolio revolutionizes the design, realization and optimization of products and production. And with Siemens Xcelerator – the open digital business platform – this process is made even easier, faster, and scalable. Together with our partners and ecosystem, Siemens Digital Industries enables customers to become a Digital Enterprise. Siemens Digital Industries has a workforce of around 70,000 people worldwide. Siemens AG (Berlin and Munich) is a leading technology company focused on industry, infrastructure, mobility, and healthcare. The company’s purpose is to create technology to transform the everyday, for everyone. By combining the real and the digital worlds, Siemens empowers customers to accelerate their digital and sustainability transformations, making factories more efficient, cities more livable, and transportation more sustainable. A leader in industrial AI, Siemens leverages its deep domain know-how to apply AI – including generative AI – to real-world applications, making AI accessible and impactful for customers across diverse industries. Siemens also owns a majority stake in the publicly listed company Siemens Healthineers, a leading global medical technology provider pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably. In fiscal 2025, which ended on September 30, 2025, the Siemens Group generated revenue of €78.9 billion and net income of €10.4 billion. As of September 30, 2025, the company employed around 318,000 people worldwide on the basis of continuing operations. Further information is available on the Internet at www.siemens.com . Note: A list of relevant Siemens trademarks can be found here . Other trademarks belong to their respective owners. Contacts for Press Siemens Digital Industries Software PR Team Email: press.software.sisw@siemens.com Further Reading Press Release all Siemens and TSMC advance AI-powered semiconductor design automation September 23, 2026 Press Release all Siemens’ Tessent ecosystem enables direct test pattern execution via PCIe and USB October 13, 2026 Press Release all Siemens digital twin technology helps Caponnetto Hueber deliver hydrodynamic innovation for Bluegame BGF45 yacht September 29, 2026

DEVELOP3D · Oct 14, 2026

AI in Product Development · CAD, CAE & Simulation

AI-generated summary: Spectral Labs reports that its AI agent has passed the Solidworks Professional exam, autonomously building, modifying and assembling parts and mechanisms in less than three hours. The company claims this makes its system the first AI to achieve an industry-standard CAD certification for mechanical engineers. It says the agent has passed the exam multiple times, although it has not yet achieved a perfect score. Founded in 2022, Spectral Labs describes itself as a spatial intelligence company developing foundation models for engineering design. Its system combines SGS-Mk2, a foundation model for physical engineering, with Quartz, its CAD execution environment. SGS-Mk2 reasons about the geometry, topology and kinematics of parts and assemblies. The company describes the model as lightweight but says it requires orchestration by a vision-language model, or VLM. A single set of model weights is conditioned and accessed through different tools within Quartz, with each tool defined around the requirements of a particular problem type. Alongside the certification result, Spectral Labs evaluated SGS-Mk2 against frontier models using 45 internally prepared CAD problems: 20 inspired by the Solidworks Associate certification, CSWA, and 25 by the Professional certification, CSWP. The CSWP-inspired problems covered all three exam segments, comprising 10 part-modeling tasks, five part-editing tasks and 10 assembly tasks. An answer counted as correct when the relevant volume, center of mass or distance was within 0.1% of the accepted value. Spectral Labs says no exam-specific training was undertaken and reports that using SGS-Mk2 reduced per-problem costs by 46–87% on problems involving geometric reasoning. CEO Rahul Iyer presents the work as a response to a shortage of long-horizon benchmarks for CAD generation, editing and understanding. The Solidworks Professional exam provides a multi-hour, multi-task evaluation rather than a collection of isolated operations. The article also notes that testing exposed issues with the exam itself and exercises that stumped other AI agents, but it does not detail those findings. The performance and cost results are reported by Spectral Labs, and the internally prepared comparison problems should be distinguished from the company’s reported success on the certification exam. Beyond the benchmark, Spectral Labs says it is expanding its work with manufacturers and growing its team. Its stated objective is to automate engineering workflows end to end, spanning design and analysis through manufacturing and sourcing, with a focus on reducing development time and production costs. The exam results demonstrate the CAD capabilities the company reports today; the broader workflow automation remains the direction of its ongoing work, rather than an outcome established by the certification exercise.

NVIDIA Newsroom · Oct 14, 2026

AI in Product Development · CAD, CAE & Simulation

News Summary: • NVIDIA introduces cuPotential, a new NVIDIA CUDA-X library developers can use to build AI agents that simulate how atoms and molecules interact, accelerating already GPU-optimized workflows by an additional 2-3x in early-access testing. • NanoVDB, an open source library for working with 3D data, accelerates chipmaking simulation for AI-defined fabs, helping Lam Research achieve 30x faster simulation. • NVIDIA NPP, a library for processing images on GPUs, accelerates chipmaking inspection for AI-defined fabs, helping KLA accelerate inspection. • Applied Materials, Cadence, KLA, Lam Research, Siemens, Synopsys and other semiconductor leaders are using NVIDIA’s accelerated computing and AI stack across chip design, manufacturing and testing, helping drive faster development, better yield and greater production capacity. NVIDIA today announced semiconductor leaders are adopting NVIDIA CUDA-X ™ libraries — including the new NVIDIA cuPotential library, which developers can use to build AI agents that simulate how atoms and molecules interact — to advance AI-defined fabs. AI-defined fabs are semiconductor factories where AI helps engineers optimize processes, solve production issues and improve production efficiency. As demand for advanced chips rises, such fabs must expand production while managing increasingly complex manufacturing processes. Engineers need new ways to model materials, analyze inspection data and test process changes in digital twins before applying them in production. The expanded CUDA-X portfolio gives AI agents and engineering applications accelerated tools for semiconductor simulation, 3D data processing and image analysis, helping automate more engineering work across chip design and manufacturing so fabs can predict process behavior and improve yield. “The semiconductor industry made the AI revolution possible. Now, AI is transforming how chips are built,” said Tim Costa, vice president and general manager of industrial and computational engineering at NVIDIA. “As fabs race to meet demand for advanced chips, NVIDIA CUDA-X and Omniverse libraries give AI agents accelerated simulation and inspection tools to help engineers manage complexity, improve yield and advance AI-defined fabs.” NVIDIA cuPotential Accelerates How Researchers Model Atomic Interactions Understanding how atoms and molecules interact is critical to modeling semiconductor materials and manufacturing processes, but these calculations can take significant amounts of time, even in software already optimized for GPUs. NVIDIA cuPotential accelerates atomic-scale simulation with specialized GPU-accelerated functions developers can add to existing software. It helps researchers model electrical forces and other interactions across large numbers of atoms to test and refine simulations faster. Synopsys is among the early adopters using cuPotential to accelerate materials simulations. By making these modeling capabilities available to AI agents, cuPotential helps agents run, evaluate and iterate on advanced molecular simulations. In early-access testing, the library accelerated already GPU-optimized workflows by an additional 2-3x. Semiconductor Leaders Advance AI-Defined Fabs With NVIDIA CUDA-X AI-defined fabs require faster ways to model chip designs, materials behavior and manufacturing processes before changes reach production. NVIDIA CUDA-X libraries — including NVIDIA cuPotential for simulating atomic interactions, NVIDIA cuEST for modeling electronic behavior and NVIDIA cuDSS for solving complex engineering simulations — accelerate these workloads to help make fabs more autonomous. Synopsys is using cuPotential, cuEST and cuDSS to accelerate semiconductor simulations, with early QuantumATK results showing a potential 30x speedup using cuEST, and S-Device benchmarks showing a 12x speedup using cuDSS. The company’s AgentEngineer solutions interoperate with NVIDIA Nemotron ™ open models and NVIDIA NeMo ™ to support AI agents for chip design and verification. Cadence is using NVIDIA accelerated computing, NVIDIA Nemotron open models and CUDA-X libraries to bring agentic AI to the design and simulation of electromechanical systems — from semiconductors to advanced packaging and printed circuit boards. Examples include Cadence AuraStack AI Super Agent optimizing advanced packages for heat and stress 8x faster using cuDSS with Cadence Celsius, and Nemotron with ChipStack AI Super Agent for semiconductor design and verification. COMSOL is accelerating semiconductor multiphysics simulations with NVIDIA cuDSS, delivering up to 10x faster simulations to help engineers optimize equipment and processes. Its upcoming COMSOL MCP Server will use the NVIDIA Nemotron Ultra open model to enable AI-assisted modeling and design exploration. Voltai accelerates advanced chip packaging with Voltaire, its agentic multiphysics platform for 3D-IC design and simulation. Voltaire combines AI agents with GPU-accelerated solvers powered by NVIDIA cuDSS and CUDA-X libraries, compressing complex electro-thermal-mechanical simulation workflows from months to a day. ChipAgents uses NVIDIA AI infrastructure and NVIDIA model-training and optimization software to develop specialized AI models for chip design. The company is also working with NVIDIA to develop Build-A-Chip Bench, a new AI benchmark that evaluates agents across end-to-end workflows from specification to graphic data system. The benchmark gives the industry a comprehensive measure of progress toward fully autonomous semiconductor engineering. NVIDIA Open Libraries Help Prepare and Optimize Manufacturing Workflows Fabs depend on thousands of process steps that must be modeled, refined and validated before production. The NVIDIA cuLitho library accelerates calculations for printing chip patterns; NanoVDB — an open source library for working with 3D data — accelerates chipmaking simulations; and the open NVIDIA PhysicsNeMo ™ framework helps train physics AI models to predict how physical systems behave. Siemens is using cuLitho to speed up Calibre software for chip-pattern refinement and mask preparation. Its Solido team is also developing agent-driven characterization workflows with Nemotron open models and NVIDIA NeMo open libraries. Lam Research intends to integrate NanoVDB into its advanced semiconductor process modeling platform SEMulator3D, part of the company’s Semiverse Solutions family of virtual products. The addition of NanoVDB is expected to accelerate select wafer-etch modeling by up to 30x, while using NVIDIA PhysicsNeMo has been demonstrated to assist in reducing plasma-prediction simulation time to as little as milliseconds instead of days. Luminary is applying NVIDIA PhysicsNeMo to semiconductor package design, training physics AI models to predict temperature, pressure drop and thermal stress across package configurations — enabling engineers to explore 100x more designs in the same schedule without running a new simulation for every option. AI-Defined Fabs Inspect and Test Chips With NVIDIA CUDA-X Libraries AI-defined fabs need to analyze inspection images and test data quickly enough to catch defects, maintain equipment and improve yield. NVIDIA NPP, a library for processing images on GPUs, and NVIDIA RAPIDS cuDF, a library for processing large datasets on GPUs, help accelerate test and quality-control workflows. Using NVIDIA AI and accelerated computing, imec is removing noise from scanning electron microscope images used for sub-nanometer chip measurements and using NVIDIA cuLitho to enable computationally intensive process-window optical proximity correction — achieving a 30% improvement in the lithography process window. KLA applied insights from its highly optimized production kernels to a new NPP NCC kernel that achieved performance levels 50x faster than earlier CPU versions, comparable with KLA’s hand-tuned implementations. Teradyne is using NVIDIA NPP and accelerated computing to accelerate image-sensor testing, and RAPIDS cuDF and CuPy for GPU calculations, to deliver approximately 85x faster probe-maintenance inference, helping identify degrading probes before they affect yield. Advantest has integrated NVIDIA Nemotron and NVIDIA Kumo-Anomaly into ACS RTDI to enable AI-driven semiconductor production analytics. Powered by NVIDIA Grace Blackwell systems, ACS delivers real-time diagnostics and inferencing that recommend corrective actions for test cells. Advantest is also using NVIDIA NeMo Switchyard AI agents to automate model selection, streamline semiconductor test workflows and reduce response times from hours to minutes. NVIDIA Technologies Advance Fab Operations and Automation In an AI-defined fab, equipment data must move from instruments into AI systems fast enough to support operational decisions. Applied Materials is using NVIDIA Omniverse™ and NVIDIA CUDA-X libraries, including cuDSS, cuEST and PhysicsNeMo, across materials discovery, process development and fab optimization — enabling up to 35x faster chamber simulations and fab digital twins that help optimize layouts and material flow. Analog Devices Inc. is combining Synopsys physics simulation with the NVIDIA Isaac Sim™ framework and Omniverse libraries to develop high-fidelity sensing simulations and robotic benchmarks, helping engineers evaluate how sensors behave within complete robotic systems. FANUC and Foxconn are connecting their robot models to the Mega NVIDIA Omniverse Blueprint, enabling manufacturers to simulate and validate robotic operations in factory digital twins before physical deployment. OMRON is using NVIDIA Cosmos™ open world models and the NVIDIA Metropolis Blueprint for video search and summarization in its inspection equipment to develop agents that combine visual data, equipment documentation and manufacturing knowledge. These agents help engineers analyze printed circuit board results, investigate potential defect causes, and support troubleshooting and quality control. Availability NVIDIA cuPotential is available in early access. NanoVDB is available as open source within OpenVDB, and NPP is available through NVIDIA CUDA Toolkit. Lam Research’s NanoVDB-accelerated SEMulator3D capability is targeted for release by the end of this year. Learn more by joining NVIDIA at SEMICON West . About NVIDIA NVIDIA (NASDAQ: NVDA) is the world leader in AI and accelerated computing. Certain statements in this press release including, but not limited to, statements as to: expectations with respect to growth, performance, availability, and benefits of NVIDIA’s products, services and technologies, and related trends and drivers; expectations with respect to NVIDIA’s third party arrangements, including with its collaborators and partners, and the development, availability and benefits of related offerings; expectations with respect to technology developments, and related trends and drivers; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the “safe harbor” created by those sections based on management’s beliefs and assumptions and on information currently available to management and are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; NVIDIA’s reliance on third parties to manufacture, assemble, package and test NVIDIA’s products; the impact of technological development and competition; development of new products and technologies or enhancements to NVIDIA’s existing products and technologies; market acceptance of NVIDIA’s products or NVIDIA’s partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of NVIDIA’s products or technologies when integrated into systems; NVIDIA’s ability to realize the potential benefits of business investments or acquisitions; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its Annual Report on Form 10-K and Quarterly Reports on Form 10-Q. Copies of reports filed with the SEC are posted on NVIDIA’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

Siemens Digital Industries Software · Oct 14, 2026

CAD, CAE & Simulation

Siemens today announced Tessent™ Silicon Executable software, a standalone application designed to translate test patterns and apply them directly onto high-speed interfaces and protocols. The ecosystem includes leading test provider partners who will embed the Tessent software within the automated test equipment providing a simplified workflow and enhanced productivity. Tessent Silicon Executable extends the Tessent workflow from design-for-test insertion, pattern generation, and test collateral creation into pattern execution in silicon. It consumes Tessent-generated collateral, applies tests through the target execution environment, and returns results that downstream Tessent debug, diagnosis and analysis flows can use. “Semiconductor testing is at an inflection point requiring new scan test delivery methods while minimizing time-to-ramp for a product to release to market. The combination of silicon complexity, multi-die and new transistor technology is driving a growth in test content,” said Ankur Gupta, executive vice president and Head of IC, Siemens EDA, Siemens Digital Industries Software. “Tessent Silicon Executable software is embedded in test equipment to directly connect test creation and execution, helping customers apply Tessent-generated patterns consistently and return results to established debug, diagnosis and analysis workflows across test environments. This enables a new level of correlation between environments and provides optimized product economics for device manufacturing” As semiconductor designs increase in complexity and operating frequency, traditional test methods can face longer test times and more demanding execution requirements. Tessent Silicon Executable software is designed to simplify test pattern application, support high-speed interface-based testing and provide a consistent approach across automated test equipment, system-level test, bench and in-system environments. Tessent Silicon Executable launches in collaboration with automated test equipment providers Advantest and Teradyne. “Advantest works with all major EDA vendors to address increasing device complexity and coverage gaps with a seamless workflow across the silicon lifecycle,” said Juergen Serrer, CTO of Advantest . “By integrating Siemens TSE into our V93000 platform with Link Scale and extending it into pre-silicon verification and DFT lab engineering with SiConic, we provide customers with a uniquely consistent path from development to production.” "In advanced packaging, how test content reaches the device matters as much as the content itself," said Shannon Poulin, president, Semiconductor Test group, Teradyne . "By integrating Siemens’ Tessent Silicon Executable into our UltraPort instrument for UltraFLEXplus, we are the first tester company to give customers a standard path to run SSN over PCIe for industry-leading PCIe Gen 6 interfaces, which dramatically improves performance and reduces test times.” Tessent Silicon Executable software is part of the broader Tessent ecosystem for semiconductor design-for-test, silicon test, debug, diagnosis and analysis. To learn more about Tessent Silicon Executable software, visit https://www.siemens.com/en-us/products/ic/tessent/yield-learning/silicon-executable/ Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens' software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today's ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation. Note: A list of relevant Siemens trademarks can be found here . Other trademarks belong to their respective owners. Contacts for Press Siemens Digital Industries Software PR Team Email: press.software.sisw@siemens.com Further Reading Press Release all Siemens and TSMC advance AI-powered semiconductor design automation September 23, 2026 Press Release all Siemens digital twin technology helps Caponnetto Hueber deliver hydrodynamic innovation for Bluegame BGF45 yacht September 29, 2026 Press Release all Siemens accelerates physical testing with Simcenter Testlab September 28, 2026

CIMdata · Oct 14, 2026

PLM Software · Standards & Regulation

This presentation is part of CIMdata’s educational webinar series. ANN ARBOR, Michigan, USA 14 October 2026—CIMdata, Inc., the leading global PLM strategic management consulting and research firm, announces an upcoming free educational webinar, “The Sustainability Investment Imperative: Where PLM Delivers the Greatest Impact.” The webinar will take place on Thursday, 12 November, at 11:00 a.m. (ET) and will last for one hour. Sustainability has not gone away; it has evolved. Corporate reporting requirements have been recalibrated, yet atmospheric CO₂ continues to rise. Product-level requirements, such as Product Carbon Footprints (PCF) and Digital Product Passports (DPP), continue to advance, and energy security, critical materials, and supply-chain resilience have become board-level concerns. Attendees at this webinar will: • Understand why sustainability remains a business imperative despite shifting political priorities. • Learn how to determine how much to invest in sustainability and where those investments deliver the greatest return. • Understand how to measure the business and environmental value of sustainability investments. • Discover how integrating sustainability into engineering decisions lowers your product’s PCF and cost. • Understand the product-data foundation needed to meet customer, market, and regulatory requirements without rebuilding information each time. • Learn how supplier data ecosystems and Digital Product Passports extend PLM beyond the enterprise. • Gain a realistic view of where AI can accelerate product sustainability. • Leave with a value-based roadmap for directing sustainability investment. According to Mark Reisig, a CIMdata Executive Consultant and Sustainability Practice Director, “Sustainability investment should not rise and fall with political fashion or reporting deadlines. The companies that gain the most will direct investment toward the products, engineering decisions, and lifecycle stages where they can create the greatest environmental and business value. PLM provides the product context and digital thread needed to make those investments measurable, repeatable, and defensible.” Mark Reisig has over 40 years of experience in digital transformation, information technology, and engineering in the energy, aerospace, defense, automotive, industrial, shipbuilding, high-tech electronics, medical devices, and food & beverage industries. He has led digital initiatives in PLM, plant design, computer-aided design, and master data management for global businesses. Mark comes to CIMdata after an extensive career in PLM with GE, Federation, Oracle, Auto-trol Technology, Kraft-Heinz, Catalytic, Day & Zimmermann, and most recently, Aras, where he was the Vice President of Product Marketing. This webinar will examine how much companies should invest in sustainability and where those investments deliver the greatest environmental and business returns. We will show how PLM, with its product context and digital thread, enables companies to direct investment toward the products, engineering decisions, and lifecycle stages that matter most, and to make those investments measurable, repeatable, and defensible. During the webinar, attendees can ask questions about the topics discussed. To find out more, visit: https://www.cimdata.com/en/education/educational-webinars/webinar-the-sustainability-investment-imperative-where-plm-delivers-the-greatest-impact . To register for this webinar, please visit https://register.gotowebinar.com/register/1862927191805102165 .

DEVELOP3D · Oct 13, 2026

CAD, CAE & Simulation

AI-generated summary: Nvidia has launched USD BRep, a standalone C++ and Python geometry library for NURBS-based boundary representation solid modelling, with native import and export of BRep data to and from OpenUSD. The article reports that the release is prompting developers to explore new open-source CAD software designed around AI tools and workflows. Its broader significance, however, may lie in connecting precise engineering geometry with visualisation, industrial digital twins and downstream applications rather than replacing established CAD systems. The AI-to-CAD problem addressed by USD BRep is the difficulty of training machine-learning algorithms on traditional CAD data, including associated metadata and multistep feature trees. The kernel exposes NURBS and BRep geometry directly through Python and C++ structures, giving developers access to exact geometric boundaries for neural-network training and generative-model development. The article presents this as a way to support large language models and diffusion networks in creating mathematically sound, manufacturable 3D models, although it does not provide examples or performance results demonstrating those outcomes. BRep represents exact three-dimensional solids through their vertices, edges and faces, while NURBS—Non-Uniform Rational B-Splines—describe smooth mathematical curves and surfaces, including cylinders and organic shapes. Unlike polygon meshes, these representations preserve the precise geometry required for engineering work. USD BRep allows that engineering data to reside natively within USD layers, simplifying OpenUSD-based visualisation and VR/XR workflows while creating opportunities for new modelling applications. Combined with Nvidia’s Omniverse platform and its framework for industrial digital twins, this integration holds the promise of enabling engineers to manipulate large industrial assets in real time without sacrificing dimensional accuracy. The article also highlights the potential to combine precise CAD geometry with the photorealistic visualisation associated with OpenUSD digital twins. These are presented as opportunities enabled by the architecture, rather than established results from a documented industrial implementation. The article nevertheless argues that established CAD kernels are unlikely to be displaced quickly. Siemens’ Parasolid has more than 30 years of development behind it, including troubleshooting and resolution of mathematical edge cases. Its use across Siemens software, Solidworks, Onshape and other products gives it an entrenched industry position that the author expects to remain important for years. Siemens Digital Industries CEO Tony Hemmelgram offers a contrasting view of AI-driven CAD: the main value is not another modeller or free kernel, but tighter connections between capable existing CAD tools and language models. He points to the APIs in NX and Designcenter and cites a project linking NX with Claude as an example of an LLM driving an established engineering application. USD BRep also builds on an existing geometry technology. The open-source project includes SMLib, the Solid Modeling Library originally developed by Solid Modeling Solutions, which Nvidia acquired in May 2022. SMLib currently powers geometry processing and solid modelling in AWV Informatik’s ClassCAD. AWV Informatik CEO Weigel Rainer, whose company has worked with the SMLib team for more than 20 years, welcomed the kernel becoming open source and expressed interest in what the wider CAD community would build with it. The author concludes that Nvidia appears more focused on improving engineering-data pipelines, translation and downstream infrastructure than on replacing industry-standard CAD tools. The immediate opportunity is to move complex BRep geometry into applications such as Omniverse without reducing it to polygon meshes and losing its precise mathematical representation. As a related development, the article notes that Unity has released a USD Toolkit in partnership with Nvidia, bringing OpenUSD support to Unity and its visual digital twins.

CIMdata · Oct 13, 2026

PLM Software · AI in Product Development

Leading solution providers to sponsor product lifecycle management (PLM) conference. ANN ARBOR, Michigan, USA & STOCKHOLM, SWEDEN – 13 October 2026—CIMdata, Inc. and HKAB announce the sponsors of the upcoming PLM Road Map & PDT EMEA 2026 event. Participating sponsors include the Aerospace & Defense PLM Action Group, AMC Bridge, Configit, Contact Software, Dassault Systèmes, PROSTEP, PTC, Share PLM, TCS, and Uncountable. The event is scheduled for 4 & 5 November at the Lindholmen Conference Center in Gothenburg, Sweden. "We are delighted to have these key solution providers join us for PLM Road Map & PDT," said Cheryl Peck, CIMdata’s Chief Marketing Officer. "Their partnership is essential to fostering cross-industry collaboration and knowledge sharing that drives the PLM community forward." As part of the PLM Road Map/PDT experience, sponsors participate in an online Collaboration Café™ to showcase their solutions. For more information about the Collaboration Café, visit https://www.cimdata.com/en/education/plm-conferences/2026-plm-road-map-pdt-emea/sponsors . PLM Road Map & PDT EMEA 2026 is a highly relevant event for PLM industry leaders and professionals. It offers independent education and a collaborative networking environment where ideas, trends, experiences, and relationships critical to the industry take root. This year’s theme is AI in PLM: A Disruptive Opportunity and Challenge - Turning AI disruption into enterprise value: Strategic insights for the PLM professional . For more details on the schedule and registration, please visit https://www.cimdata.com/en/events/plm-conferences/event/909-2026-plm-road-map-pdt-emea .

CIMdata · Oct 13, 2026

PLM Software · Digital Thread & Digital Twin

The white paper details the technical imperatives, architecture requirements, and strategic roadmap for high-tech electronics manufacturers transitioning away from legacy PLM platforms. ANN ARBOR, Michigan, USA, 13 October 2026—CIMdata, Inc., the leading global Product Lifecycle Management (PLM) strategic management consulting and research firm, announces the release of a white paper titled “ PLM for Tomorrow’s Competitive High-Tech Electronics Enterprises: Addressing Legacy PLM Solution End-of-Life .” Sponsored by Siemens Digital Industries Software (Siemens), the white paper examines how high-tech electronics (HTE) organizations can transform forced legacy software sunsetting into an enterprise-wide modernization opportunity. As legacy PLM platforms approach mandatory end-of-life (EOL) milestones, high-tech electronics manufacturers face mounting operational risks, frozen codebases, vulnerabilities in custom middleware, and cybersecurity exposure. Over the past decade, HTE requirements have outgrown traditional, disconnected Bill of Materials (BOM) databases. Managing high-density board layouts, extreme product variability, and rapid component obsolescence now demands flexible deployment architectures, closed-loop digital threads, multi-domain CAD/EDA integration, real-time supply chain intelligence, and enterprise artificial intelligence. “The announcement of a legacy PLM solution’s end-of-life is more than an IT software replacement event; it presents a vital opportunity to modernize core engineering practices and shed decades of technical debt,” noted Tom Gill, CIMdata Principal Consultant and PLM Enterprise Value and Integration Program Manager. “By adopting open, multi-domain application platforms like the Siemens Xcelerator portfolio, anchored by Teamcenter and Supplyframe, high-tech electronics companies can seamlessly connect mechanical design, electronic layout, live global supply chain intelligence, and governed industrial AI across the product lifecycle.” The white paper applies CIMdata’s solution provider-neutral Enterprise Application Architecture™ (EAA) framework to outline critical platform capabilities required for modern enterprise innovation. It evaluates core functional areas, including cloud-native Software-as-a-Service (SaaS) and on-premises deployment flexibility, closed-loop digital threads connecting design to factory execution (via Teamcenter, Simcenter, and Opcenter), design-to-source component intelligence (via Supplyframe), and semantic industrial AI (via Siemens Intelligence Center X). The paper also details low-risk migration frameworks like Siemens Advantedge, showing how organizations can execute a rapid “lift and shift” migration to secure data today while laying the foundation for long-term digital transformation. The CIMdata white paper, “PLM for Tomorrow’s Competitive High-Tech Electronics Enterprises: Addressing Legacy PLM Solution End-of-Life,” is available for download from the CIMdata website at www.CIMdata.com .

Engineering.com · Oct 12, 2026

PLM Software · Digital Thread & Digital Twin

AI-generated summary: Engineering.com discusses BOM synchronization between ECAD, PLM and ERP systems. The article presents establishing a digital thread from initial design to the final purchase order as a practical approach. It also addresses how cloud platforms bring enterprise data together and raises the question of BOM management software for PCB design teams. The supplied table distinguishes the responsibilities of ECAD and PLM: ECAD captures schematic, layout and electrical intent, while PLM manages product structure, revisions and compliance. It associates electrical engineers with selecting active, compliant and in-stock parts, and product managers with maintaining a single source of truth for the complete assembly. For product-development teams, the excerpt frames synchronization around connecting these distinct design and product-data responsibilities rather than announcing a new software release.