New-Tech Europe | Q3 2026 | Digital Edition

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Q3 2026

16 Five Semiconductor Trends Defining the Next Generation of Computing 20 Beyond Industry 4.0: How Industrial AI Is Reshaping Europe’s Smart Factories 24 Is RF Design Ready for Its Biggest Methodological Shift in Decades? 28 EDA AI Agents: Intelligent Automation in Semiconductor & PCB Design

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Microchip Single Pair Ethernet Connects With Remote Control Protocol Smarter, Centralized Control With the LAN866x The LAN866x family of 10BASE-T1S Remote Control Protocol (RCP) enabled endpoints streamlines Ethernet network design by delivering efficient and secure functionality directly at the edge nodes. This centralized approach eliminates the need for code on each node, accelerating time to market. These endpoints are ideal for use within a zonal network and to enable an all-Ethernet architecture. Microchip’s VelocityDRIVE ™ portfolio of automotive Ethernet products offers a comprehensive range of complementary Single-Pair Ethernet (SPE) PHYs, switches and bridges, making Microchip your one-stop shop for all your SPE solution needs. Key Features • OPEN Alliance Remote Control Protocol (RCP) and SOME/IP • gPTP • TC10/14 • ISO26262 ASIL B • AEC-Q100 Grade 1 (-40°C to 125°C)

The Microchip name and logo and the Microchip logo are registered trademarks of Microchip Technology Incorporated in the U.S.A. and other countries. All other trademarks are the property of their registered owners. © 2026 Microchip Technology Inc. All rights reserved. MEC2641A-UK-01-26

microchip.com/LAN8660

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Functional Block Diagram Functional Block Diagram

Overview

Target Applications • AI CCTV •ADAS • Industrial Robot Arms • AI Dashcams • Autonomous Mobile Robots (AMRs) •Drones • Edge Servers • AIoT Devices • Remote Patient Monitoring (RPM) 2) AI Model Runtime Environment Deploys optimized models onto hardware via DX-STREAM™ and DX-RT engines using C++ or Python APIs. Step 2 Step 3 Auto Install & Update Memory Integrated 2GB LPDDR4x (4266 MT/s) Power Consumption 3W (Typical) Operating Temperature -40 ~ 85°C (Industrial) Thermal Solution Heatsink (Option) Form Factor OS Support Windows 11/10 Debian-based Linux (Ubuntu 24.04/22.04/20.04 LTS) Yocto Project AI Frameworks Ultralytics, TensorFlow, PyTorch, ONNX, Keras System Support x86, ARM Based Architecture Experience the future of on-device AI with the DEEPX DX-M1M . This AI processor delivers a stunning 25 TOPS while maintaining an ultra low power consumption of just 3W(Typical). We prioritize Inferences Per Second (IPS) per Watt, so every operation delivers maximum, real-world value. The DX-M1M integrates effortlessly with your host system, accelerating diverse applications like computer vision and audio analysis . Details 25 TOPS (INT8) PCIe Gen3 x2 (Supports Gen 1/2/3 & x1/x2) Target Applications • AI CCTV •ADAS • Industrial Robot Arms • AI Dashcams • Autonomous Mobile Robots (AMRs) •Drones • Edge Servers • AIoT Devices • Remote Patient Monitoring (RPM) Finalize & Start Development Docker Install Experience the future of on-device AI with the DEEPX DX-M1M . This AI processor delivers a stunning 25 TOPS while maintaining an ultra low power consumption of just 3W(Typical). We prioritize Inferences Per Second (IPS) per Watt, so every operation delivers maximum, real-world value. The DX-M1M integrates effortlessly with your host system, accelerating diverse applications like computer vision and audio analysis . DX-AllSuite Specifications Features AI Performance Host Interface M.2 2242 (M+B Key), 22 x 42mm

QSPI Flash (Nand) 1Gbit

UART SWD

QSPI Flash (Nand) 1Gbit

UART SWD

M.2 PCIe Gen3 x2

3 Color LED

M.2 PCIe Gen3 x2

3 Color LED

Specifications

Details Overview Experience the future of on-device AI with the DEEPX DX-M1M . This AI processor delivers a stunning 25 TOPS while maintaining an ultra-low power consumption of just 3W(Typical). We prioritize Inferences Per Second (IPS) per Watt, so every operation delivers maximum, real-world value. 25 TOPS (INT8) Details PCIe Gen3 x2 (Supports Gen 1/2/3 & x1/x2) 25 TOPS (INT8) 1) AI Model Compile Environment Optimizes models from various sources into DEEPX-specific .dxnn formats via the ONNX framework.

Features Specifications

AI Performance

Features

Host Interface AI Performance

Memory Host Interface

Integrated 2GB LPDDR4x (4266 MT/s) PCIe Gen3 x2 (Supports Gen 1/2/3 & x1/x2)

Power Consumption

3W (Typical)

Memory

Integrated 2GB LPDDR4x (4266 MT/s)

Step 1

Operating Temperature -40 ~ 85°C (Industrial) DX-AllSuite

Supported Environments

Power Consumption

3W (Typical)

Thermal Solution DX-AllSuite: The Single Package for Your Complete DXNN Environment. Simplify setup, installation, and updates across local machines and Docker. Form Factor

Heatsink (Option)

Operating Temperature -40 ~ 85°C (Industrial)

Developers

AWS IoT Greengrass

M.2 2242 (M+B Key), 22 x 42mm

Local Install

Thermal Solution

Heatsink (Option)

Windows 11/10

Form Factor

M.2 2242 (M+B Key), 22 x 42mm

מלאי בחברת

OS Support

Debian-based Linux (Ubuntu 24.04/22.04/20.04 LTS) Amir Sherman Sales & Business Development EMEA +972 52 2240811 +49 173 3232288 as@deepx.ai USA 1735 Technology Drive Suite 740. San Jose, CA U.S.A Yocto Project Windows 11/10 Ultralytics, TensorFlow, PyTorch, ONNX, Keras Debian-based Linux (Ubuntu 24.04/22.04/20.04 LTS)

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Q3 2026

About New-Tech Magazines Group Read To Lead ‘New-Tech Magazines’ A world leader in publishing high-tech and electronics, producing top quality publications read by tens of thousands professionals from all over the world especially from Europe, innovative electronics, IoT, microwave, homeland security, aerospace, automotive and technological industries. Our specialized target audiences prefer New-Tech Europe because they know that our publications are a reliable source of the latest information in their respective fields. Our multidimensional editorials, news items, interviews and feature articles provide them with a full, well-rounded picture of the markets in which they operate - an essential asset for every technological leader striving to stay ahead, make the right decisions, and generate the next global innovation. Moreover, as an attractive platform for advertisers from around the world, New-Tech Europe has become a hub for bustling international commercial activity. Here, through ads and other promotional materials, Israeli readers obtain crucial information about developers and manufacturers worldwide, finding the tools, instruments, systems and components they need to facilitate their innovative endeavors. Targeting the needs of both the global and european industries and global advertisers, New-Tech Magazines Group constantly expands and upgrades its services. Over the years, the company has been able to formulate a remarkably effective, multi-medium mix of offerings, combining magazine publications with useful online activities, newsletters and special events and exhibitions.

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8 l New-Tech Magazine Europe

Contents

10 LATEST NEWS

Five Semiconductor Trends Defining the Next Generation of Computing Beyond Industry 4.0: How Industrial AI Is Reshaping Europe’s Smart Factories Is RF Design Ready for Its Biggest Methodological Shift in Decades?

EDA AI Agents: Intelligent Automation in Semiconductor & PCB Design

Beyond Copper: Why Integrated Photonics Is Key to Scaling AI Infrastructure

36 When the PCB Becomes Part of the Problem

42 OUT OF THE BOX

44 NEW PRODUCTS

52 INDEX

www.new-techeurope.com

New-Tech Magazine Europe l 9

Latest News

Samsung and Broadcom Expand AI Chip Collaboration, Partnership Could Exceed $200 Billion

Samsung Electronics and Broadcom have signed a memorandum of understanding (MoU) to expand their collaboration across advanced memory, semiconductor manufacturing and advanced packaging technologies. The initiative is aimed at supporting the next generation of artificial intelligence infrastructure, including AI accelerators, networking chips and high-performance computing (HPC) platforms. According to Samsung, the cumulative value of the companies’ collaboration in memory and semiconductor manufacturing could exceed $200 billion over the next five years, through 2030. The company emphasized that this figure represents the projected scale of the planned partnership rather than a single purchase agreement or a binding financial commitment. In the memory segment, the companies plan to expand their cooperation on advanced memory solutions, including High Bandwidth Memory (HBM), for future generations of Broadcom’s AI accelerators. HBM has become a critical component in AI systems and data centers by enabling high-speed data transfer between processors and memory. Within the foundry business, the collaboration will focus on Samsung’s 2-nanometer and more advanced process technologies. These manufacturing nodes are expected to support Broadcom products for wireless communications, connectivity and AI infrastructure applications. The companies also plan to integrate 2.3D and 2.5D advanced packaging technologies, enabling logic chips, memory and communication components to be combined

within a single package. The approach is designed to increase bandwidth, improve overall system performance and reduce power consumption. Young Hyun Jun, Vice Chairman and Head of Samsung Electronics’ Device Solutions Division, said that the rapid growth of AI is driving the need for tighter integration between memory, logic and advanced packaging. He added that the expanded collaboration with Broadcom will help deliver more integrated solutions for future AI infrastructure. Charlie Kawwas, President of Broadcom’s Semiconductor Solutions Group, said that as AI infrastructure continues to scale, collaboration across the semiconductor ecosystem is becoming increasingly important to accelerate innovation. The agreement could further strengthen Samsung’s position as an integrated supplier for the AI industry. The company is investing simultaneously in memory, foundry and advanced packaging technologies as it seeks to narrow the gap with TSMC in advanced semiconductor manufacturing and with SK hynix in the HBM memory market.

Credit: Samsung Electronics

Humanoid Becomes Europe’s First Pure-Play Humanoid Robotics Unicorn Following $152 Million Series A

British robotics company Humanoid has raised $152 million in Series A funding, reaching a post-money valuation of $1.35 billion and becoming Europe’s first pure-play humanoid robotics unicorn, just two years after its founding.

The funding round, led by Prime Movers Lab, with participation from Schaeffler, Bosch, Fubon Financial Holding Venture Capital, and Aglaé Ventures, brings the company’s total funding to $270 million. The investment will accelerate the development of

New-Tech Magazine Europe l 11

Latest News Humanoid’s next-generation AI-powered humanoid robots, expand commercial deployments with industrial customers, and further advance KinetIQ, the company’s proprietary artificial intelligence platform designed to enable general-purpose humanoid robots capable of operating in real-world industrial environments. The company plans to begin long-term commercial beta deployments during the fourth quarter of 2026, followed by the large-scale production of its wheeled humanoid robots. Initial deployments will focus on manufacturing, logistics, retail and other industries facing persistent labour shortages and increasing automation demands. In just two years, Humanoid has established partnerships with several global industrial and technology leaders, including SAP, NVIDIA, Bosch and Siemens. The company has also signed what it describes as the industry’s largest publicly announced commercial deployment agreement with Schaeffler, paving the way for the large-scale deployment of thousands of humanoid robots across manufacturing facilities. “Within just two years, we have transformed Humanoid from an idea into Europe’s first pure-play humanoid robotics unicorn,” said Artem Sokolov, Founder and CEO of Humanoid. “This funding enables us to accelerate the transition of humanoid robots from cutting-edge technology to practical industrial tools that solve real operational challenges.” According to Prime Movers Lab, humanoid robotics is expected to become one of the most transformative technology sectors Intel has reported its financial results for the second quarter of 2026, posting revenue of $16.1 billion, a 25% year-over-year increase and the company’s strongest revenue growth in more than 15 years. Adjusted earnings per share reached $0.42, while Intel expects third-quarter revenue to range between $15.8 billion and $16.8 billion. According to Intel CEO Lip-Bu Tan, accelerating demand for artificial intelligence solutions continues to drive the company’s growth. He said the combination of rising demand for compute capacity, improved manufacturing capabilities and the expansion of Intel’s semiconductor manufacturing operations positions the company to further

of the coming decade, with only a limited number of companies in the United States, Europe and China likely to emerge as global leaders. Founded in 2024, Humanoid employs more than 250 engineers, researchers and AI specialists across offices in London, Boston, Vancouver and San Diego. The company develops industrial humanoid robots powered by its proprietary KinetIQ AI platform, combining advanced robotics hardware with autonomous decision-making capabilities for deployment in real industrial environments. The announcement marks another milestone for Europe’s rapidly expanding robotics ecosystem, highlighting growing investor confidence that humanoid robots are moving beyond research laboratories toward scalable commercial deployment in industrial applications.

Credit: Business Wire / Humanoi

Intel Returns to Growth as Revenue Jumps 25% on Strong AI Demand and Manufacturing Expansion grow its CPU business, ASIC portfolio, advanced packaging technologies and Intel Foundry services.

Chief Financial Officer David Zinsner said Intel exceeded market expectations thanks to strong customer demand and continued improvements in manufacturing performance. Higher factory yields and shorter production cycle times enabled the company to increase output, while demand for AI-driven computing solutions continued to strengthen. As a result, Intel announced plans to increase investments in manufacturing equipment, cleanroom capacity and wafer production to support expected growth throughout the remainder of 2026 and into 2027. The company’s strongest growth drivers during the quarter were its Data Center and AI business, which

12 l New-Tech Magazine Europe

Latest News expects ongoing investment in AI infrastructure to support further growth across both its product portfolio and Intel Foundry manufacturing services.

Company surpasses $1.5 billion ARR as profitable growth fuels its 15th acquisition to accelerate trusted AI infrastructure for global work Deel, the global people platform trusted by 40,000+ businesses to hire, manage, and pay teams across 150+ countries, today announced it has acquired Clarity, an AI cybersecurity company specializing in identity verification, deepfake detection, and fraud prevention. The acquisition extends Deel’s trusted identity capabilities across the workforce lifecycle – from pre-hire identity verification and background checks through post-hire device provisioning and secure workforce access – helping organizations navigate a new generation of AI-driven security threats. The acquisition coincides with Deel announcing that it surpassed $1.5 billion in Annual Recurring Revenue (ARR) in H1’26, reflecting the company’s continued profitable growth and providing the scale to accelerate strategic AI investments and disciplined acquisitions. Together, these milestones demonstrate how Deel is reinvesting profitable growth into the technologies that will define the future of global work. Deel is investing not only in AI-powered productivity, but also in the trust and security organizations need to adopt AI with confidence. “AI is changing every part of how companies build global teams, but it’s also changing the threat landscape,” said Alex Bouaziz, Co-founder and CEO of Deel. “Organizations need more than point-in-time verification; they need continuous trust. Bringing generated $6.3 billion in revenue, up 59% year over year, and Intel Foundry, which reported $5.8 billion in revenue, a 31% increase. Intel’s Client Computing and Physical AI business also continued to expand, reaching $8.9 billion in revenue. During the quarter, Intel further expanded its Agentic AI infrastructure strategy by introducing new distributed inference solutions powered by Xeon processors. Together with SambaNova and Foxconn, the company demonstrated production-ready AI infrastructure designed for large-scale AI workloads and multi-agent systems. Intel also continued to broaden its collaborations with leading companies across the industrial, healthcare and automation sectors. Intel’s outlook for the third quarter reflects continued strong demand for advanced computing solutions. The company

Credit: Intel.

Deel Acquires AI Cybersecurity Pioneer Clarity to Build Trusted Identity for the AI Era

Clarity and its world-class AI talent into Deel gives us the capabilities to build trusted identity security directly into our platform. This lets customers confidently hire, onboard, and manage verified teams globally, with continuous safeguards that evolve as threats get smarter.” As part of the acquisition, Clarity’s proprietary AI technology and elite engineering team will join Deel, accelerating the company’s ability to build AI-native identity and security capabilities in house. By connecting pre-hire identity verification with post hire device provisioning and workforce access through Deel IT, Deel is creating a trusted identity layer spanning the entire workforce lifecycle. The acquisition also complements Deel’s existing ecosystem of background check partners, combining best-in-class verification with proprietary AI to help customers securely hire, onboard, and manage global teams. “Trust shouldn’t end after a background check on day one,” said Michael Matias, Co-founder and CEO of Clarity. “We built Clarity to continuously verify identity in an era where AI makes fraud more convincing than ever. Joining Deel allows us to bring that vision to one of the world’s largest global workforce platforms and help organizations confidently embrace AI while strengthening security across the entire employee journey.” The acquisition marks Deel’s 15th acquisition and reflects the company’s disciplined approach to expanding its platform through strategic M&A. This approach enables Deel to bring differentiated AI capabilities in-house while preserving

New-Tech Magazine Europe l 13

Latest News customer choice and maintaining a capital-efficient growth strategy. The way the world works has changed – and Deel has built the standard to power it all. We make it effortless to hire, manage, pay, and equip any worker, anywhere. Deel is one platform for payroll, HR, benefits, mobility, performance, and device management across 150+ countries. Built on owned infrastructure, powered by AI, and supported by thousands of local experts, Deel helps businesses scale smarter, faster, and more compliantly. Trusted by 40,000+ customers, and created to become a global brand people love. Mobileye is entering one of the most significant periods in its history. Alongside stronger-than expected second-quarter financial results and an improved outlook for 2026, the company announced that founder Professor Amnon Shashua will step down as Chief Executive Officer after more than 27 years in the role. At the same time, Mobileye unveiled an expanded collaboration with Stellantis, reinforcing its position in the global market for advanced driver assistance systems (ADAS). Shashua, who founded Mobileye in 1999 and transformed it from a Jerusalem-based startup into one of the world’s leading automotive technology companies, will remain Chairman of the Board. In his new role, he will focus on the company’s long-term technology strategy, including autonomous driving, robotaxi platforms and AI-powered mobility solutions. For the second quarter of 2026, Mobileye reported revenue of $508 million, exceeding market expectations, while raising its full-year revenue and profitability guidance. The company said demand for its technologies remains strong despite continued challenges across the global automotive industry, citing improvements in both operating performance and profitability. On the commercial front, Mobileye also announced a new agreement with Stellantis, under which it will provide its Road Experience Management (REM) technology. The platform uses data collected from millions of connected vehicles to generate

Image: Clarity Team | Credit: Elhanan Moshkovitz

Mobileye Enters a New Chapter as Founder Amnon Shashua Steps Down as CEO, Raises Outlook and Expands Stellantis Partnership

continuously updated, high-definition road maps that support advanced driver assistance systems and future autonomous driving applications. With Stellantis joining the platform, five of the world’s ten largest automakers are expected to use Mobileye’s REM technology. According to the company, the platform now covers more than 95% of the road network across the United States and Europe, making it one of Mobileye’s core assets as it advances next-generation autonomous driving technologies. The combination of leadership transition, improved financial performance and expanded partnerships with major global automakers marks an important new phase for Mobileye as it continues to strengthen its software, mapping and AI capabilities while expanding its role in the future of intelligent mobility.

Image: Mobileye President and CEO Prof. Amnon Shashua | Credit: Walden Kirsch/Intel Corporation

14 l New-Tech Magazine Europe

Latest News

Quantum Machines Makes Second European Acquisition in Six Weeks as Quantum Closes In on Real-World Advantage

This acquisition further establishes Quantum Machines as the quantum company with the broadest global footprint, with employees across 22 countries. Quantum Machines (QM), whose control systems are used by more than half of the world’s quantum computing companies, today announces the acquisition of Hungarian company PCB Engineering – its second European acquisition in six weeks. The deal establishes a new Budapest R&D hub, allowing Quantum Machines to accelerate its roadmap as quantum advantage appears closer than ever. With employees in 22 countries and major offices across the U.S., Denmark, Germany, Israel, Japan, Singapore, the Netherlands, and now Hungary, Quantum Machines has built the quantum industry’s broadest global footprint. Quantum Machines is deepening its hybrid quantum-classical control architecture that the industry depends on to turn QPUs into useful quantum computers. The company’s activities span different modalities (neutral atoms, superconducting qubits, trapped ions, spin qubits, etc.) as well as different segments (hyper-scalers, data-centers, national labs, university labs, startups, etc.) and therefore demand vast investments and an extremely high pace of innovation. Itamar Sivan, co-founder and CEO of Quantum Machines, said: “Quantum computing is almost reaching its turning point – and unprecedented impact is around the corner. It won’t be long until fault-tolerant quantum computers are a reality. To get there, Quantum Machines has built the industry’s biggest quantum control team and is deploying the biggest investments in quantum control, by far.” Janos Lazanyi, founder and CEO of PCB Engineering, said: “For two decades, our engineering team has designed high-speed, high-density systems and complex hardware architecture at the forefront of high-performance computing. Becoming part of Quantum Machines lets our engineers leverage their know how to help build the hardware that quantum computing depends on. And we are especially proud that, together with QM, Hungary’s tech and talent will play a central role in shaping the future of quantum computing.” Shaul Galila, COO of Quantum Machines, said: “When you are

running a quantum computer, the control hardware has to be the strongest link – it has to perform precisely and reliably, every time. Our hardware meets that standard because of the world-class team behind it, and these engineers make it even stronger – with a proven track record of turning the most demanding designs into hardware that holds up in the real world, exactly the discipline our customers depend on as they scale.” Quantum Machines closed a $170 million Series C in 2025, bringing total funding to $280 million. As global investment in quantum computing accelerates, with leading developers committing tens of billions to fault-tolerant systems this decade, the question is no longer whether quantum will scale, but what is built around the qubit to make that scaling work. Quantum Machines (QM) is the leading global provider of hybrid quantum-classical control solutions. The company’s flagship Orchestration Platform harmonizes quantum and classical operations to eliminate friction and optimize performance across the entire stack. By providing a unified hardware and software infrastructure that supports any qubit modality, QM empowers researchers and builders to iterate at speed, resolve setbacks, and scale systems previously thought impossible.

Image: From left to right: János Lazányi, CEO of PCB Engineering; Hila Manoach, Chief People Officer at Quantum Machines; Shaul Galila, Chief Operating Officer (COO) at Quantum Machines; and Itamar Sivan, CEO and co-founder of Quantum Machines | Credit: Quantum Machines

New-Tech Magazine Europe l 15

Five Semiconductor Trends Defining the Next Generation of Computing

by New-Tech Magazine Group

Artificial intelligence, advanced manufacturing, high-bandwidth memory and new packaging technologies are reshaping the semiconductor industry. Based on the TechInsights Semiconductor Outlook 2026, this article explores five trends that are redefining how chips are designed, manufactured and deployed.

time, chiplet architectures, high-bandwidth memory, advanced packaging and shifting geopolitical priorities are reshaping how next-generation computing platforms are built. Innovation is increasingly taking place at the system level, where manufacturing technology, processor architecture, memory, packaging and software evolve together rather than as separate disciplines. The numbers illustrate the scale of this transformation. Following a record 2025, when the global semiconductor market reached approximately $796 billion, industry forecasts continue to point toward strong growth driven primarily by AI infrastructure, hyperscale data centres, advanced memory and packaging technologies. Under its most optimistic scenario, the TechInsights Semiconductor Outlook 2026 suggests the market could exceed $2 trillion before the end of the decade, reflecting the unprecedented level of investment now flowing into semiconductor technologies. Against this backdrop, the TechInsights report identifies five developments that are expected to define the industry’s next phase of growth. Together, they reveal a broader shift in semiconductor innovation, one where competitive advantage depends not on a single technological breakthrough, but on the successful integration of multiple technologies across the entire computing ecosystem.

For decades, progress in the semiconductor industry was measured by one defining metric: transistor scaling. Each new process node delivered higher performance, greater transistor density and improved energy efficiency, extending the trajectory described by Moore’s Law. That model continues to evolve, but it no longer explains the industry’s direction on its own. Artificial intelligence has fundamentally changed the economics of semiconductor development. At the same

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1. AI Is Rewriting the Economics of Chip Development For more than a decade, smartphones were the semiconductor industry’s primary growth engine. Today, that role belongs to artificial intelligence. The rapid expansion of generative AI, foundation models and high-performance computing has created unprecedented demand for advanced semiconductor devices. The effects extend far beyond AI accelerators themselves, influencing nearly every layer of the semiconductor value chain. Industry projections indicate that the four largest US cloud providers are expected to invest approximately $718 billion in computing infrastructure and data centres during 2026. Those investments include far more than AI processors. They encompass high-bandwidth memory, ultra-fast networking, advanced packaging technologies and the infrastructure required to deploy increasingly sophisticated AI systems at scale. The nature of processor development is changing as well. While general-purpose CPUs remain indispensable, the strongest growth is occurring in specialised hardware designed specifically for machine learning workloads, including GPUs and application-specific integrated circuits (ASICs). At the same time, demand continues to rise for high-speed interconnects, advanced storage technologies and high-bandwidth memory, all of which are essential for feeding AI systems with the enormous volumes of data they require. Another significant shift is taking place inside the world’s largest cloud companies. Rather than relying exclusively on merchant silicon vendors, hyperscalers are increasingly designing their own processors. Google’s Tensor Processing Units (TPUs), Amazon Web Services’ Trainium family and Microsoft’s Maia accelerators illustrate how semiconductor design has become a strategic capability for cloud infrastructure providers rather than remaining the exclusive domain of traditional chip manufacturers. The result is a profound change in industry priorities. Artificial intelligence is no longer simply another application for semiconductors. It has become the primary force directing investment across processor architectures, manufacturing technologies, memory development, packaging strategies and networking infrastructure. Meeting AI’s rapidly growing computational demands, however, requires more than larger data centres and increasingly powerful accelerators. It also depends on continued advances in semiconductor manufacturing, where each new process generation must deliver higher performance while operating within increasingly demanding power and thermal constraints.

2. Why 2 nm Is More Than Another Process Node This is precisely where the transition to 2 nm becomes significant. For decades, each new manufacturing node was largely associated with smaller transistors and higher transistor density. The move to 2 nm represents a much broader technological transition. Further progress now depends on combining several major innovations, including Gate-All-Around (GAA) nanosheet transistors, backside power delivery, advanced materials and increasingly sophisticated manufacturing techniques. Together, these technologies enable further improvements in performance and energy efficiency even as conventional transistor scaling becomes progressively more difficult and expensive. The first generation of 2 nm manufacturing is expected to deliver substantial gains in performance while reducing power consumption, particularly for AI accelerators, high-performance processors and hyperscale data centre applications. Achieving these improvements, however, comes at an extraordinary cost. Developing leading-edge manufacturing technologies now requires investments measured in tens of billions of dollars for a single fabrication facility, limiting participation to only a handful of companies worldwide. TSMC is widely expected to lead volume production of 2 nm devices for customers including Apple and NVIDIA, while Samsung and Intel continue accelerating their own advanced manufacturing roadmaps as competition intensifies across the global foundry market. The race is no longer focused solely on technological leadership. Equally important is the ability to attract the next generation of AI, cloud computing and high-performance computing customers. Not every application, however, requires the industry’s most advanced manufacturing node. Industrial automation, automotive electronics, communications infrastructure and many embedded systems will continue to rely on mature process technologies for years to come. These applications often place greater value on reliability, manufacturing stability, long product lifecycles and cost efficiency than on maximum transistor density. As a result, the semiconductor industry is evolving into a far more diverse manufacturing landscape. Leading-edge nodes will power the most demanding AI workloads, while mature technologies will remain essential across industrial, automotive and communications markets. Competitive leadership is therefore being redefined. Process technology remains a critical differentiator, but it is

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One of the most significant architectural changes is the growing adoption of sophisticated logic base dies, manufactured using advanced foundry processes rather than conventional DRAM fabrication alone. This logic layer provides higher I/O performance, improved memory management and greater flexibility for future AI systems. It also creates a direct link between advanced logic manufacturing, memory innovation and next-generation packaging technologies. " "

no longer sufficient on its own. Success increasingly depends on the ability to combine advanced manufacturing with efficient chip design, sophisticated packaging technologies, advanced memory integration and a complete development ecosystem capable of supporting the next generation of computing platforms. 3. HBM4 Turns Memory into a Competitive Advantage For years, processors dominated discussions about semiconductor innovation. In the AI era, memory has become equally critical. As AI models continue to grow in size and complexity, moving data efficiently has become almost as important as processing it. Memory bandwidth is now one of the defining factors in overall system performance, making high-bandwidth memory one of the industry’s most strategic technologies. HBM (High Bandwidth Memory) was developed specifically to address this challenge. By vertically stacking multiple DRAM dies using Through-Silicon Via (TSV) technology and placing memory close to the processor, HBM delivers dramatically higher bandwidth while reducing latency and improving energy efficiency compared with conventional DRAM architectures. The industry is now preparing for the next step. HBM4 is expected to deliver higher capacity, increased bandwidth and improved scalability for the next generation of AI accelerators and high-performance computing platforms. The transition, however, involves far more than introducing a faster memory device. One of the most significant architectural changes is the growing adoption of sophisticated logic base dies, manufactured using advanced foundry processes rather than conventional DRAM fabrication alone. This logic layer provides higher I/O performance, improved memory management and greater flexibility for future AI systems. It also creates a direct link between advanced logic manufacturing, memory innovation and next-generation packaging technologies. As AI deployments continue to accelerate, HBM has become one of the fastest-growing segments of the memory market. SK hynix, Samsung and Micron are investing billions of dollars to expand production capacity while developing future HBM

generations. For processor manufacturers, memory availability has become a strategic consideration that directly influences the delivery of AI accelerators to market. The industry’s approach to memory is changing accordingly. Memory is no longer a supporting component that follows processor development. It is becoming an integral part of system architecture, designed alongside processors, manufacturing technologies and advanced packaging. As AI systems continue to scale, overall performance will increasingly depend on how effectively these elements operate as a single platform. That growing level of integration naturally leads to the next transformation: the package itself is becoming as important as the silicon it contains. 4. Advanced Packaging Moves to the Centre of Semiconductor Innovation For decades, packaging was viewed as the final stage of semiconductor manufacturing. Today, it is one of the industry’s primary innovation drivers. As transistor scaling becomes more challenging and economically demanding, semiconductor companies are finding new ways to improve performance by redesigning how chips are integrated rather than relying solely on smaller process nodes. The most visible example is the widespread adoption of chiplet architectures. Instead of integrating every function onto a single monolithic die, designers increasingly divide complex processors into multiple specialised chiplets that are combined within a single package. This modular approach allows each function to be manufactured using the process technology best suited to its requirements, improving yields, reducing development costs and accelerating product development. Making this approach practical depends on equally sophisticated packaging technologies. Silicon interposers, hybrid bonding and advanced three dimensional integration techniques enable extremely high bandwidth communication between chiplets while reducing latency and power consumption. The package is no longer simply protecting the silicon. It has become an active part of the computing architecture itself.

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The AI boom has highlighted just how strategically important packaging has become. Demand for AI accelerators has, at times, been constrained not by wafer fabrication capacity but by the availability of advanced packaging technologies such as TSMC’s CoWoS platform. This has prompted foundries and semiconductor manufacturers to invest heavily in expanding advanced packaging capacity, recognising that future competitiveness depends as much on packaging as on wafer production. The shift extends well beyond manufacturing. Modern AI systems increasingly combine CPUs, GPUs, HBM stacks, networking interfaces and custom accelerators within a single package. Designing these heterogeneous systems requires close collaboration across processor architecture, memory, packaging and thermal engineering. System performance is increasingly determined by how effectively these technologies work together rather than by the capabilities of any individual device. Advanced packaging has therefore evolved from a manufacturing step into a core engineering discipline. As computing platforms become more complex, it will play an increasingly central role in determining performance, scalability and energy efficiency. The growing complexity of these integrated systems also raises another challenge. Building the world’s most advanced computing platforms now depends on a highly interconnected global supply chain. 5. Geopolitics Becomes a Strategic Design Constraint For decades, the semiconductor industry was built around a highly specialised global supply chain. Chip design was concentrated largely in the United States, advanced manufacturing in Taiwan and South Korea, while assembly, packaging and testing were distributed across Southeast Asia. This global model maximised efficiency by allowing each region to specialise in different parts of the value chain. That model is now evolving. The Covid-19 pandemic, geopolitical tensions and the strategic importance of advanced semiconductors exposed the vulnerability of highly concentrated supply chains. National security, technological sovereignty and supply chain resilience have become central considerations alongside traditional business and engineering priorities. Governments have responded with ambitious industrial strategies. The United States, the European Union, Japan and several other countries are investing heavily in domestic semiconductor manufacturing through subsidy programmes, tax incentives and long-term industrial policies. At the same

time, semiconductor companies are expanding production across multiple regions in an effort to reduce dependence on individual manufacturing locations. Complete technological independence, however, is neither practical nor desirable. Advanced semiconductor development depends on an ecosystem that spans electronic design automation software, manufacturing equipment, materials, intellectual property, wafer fabrication and advanced packaging. These capabilities remain distributed across multiple regions, making international collaboration essential to continued innovation. The industry’s challenge is therefore shifting from maximising efficiency to balancing efficiency with resilience. Future semiconductor leadership will depend not only on technological excellence, but also on the ability to build secure, diversified and flexible supply chains capable of supporting increasingly complex AI-driven computing platforms. Conclusion The five trends identified in the TechInsights Semiconductor Outlook 2026 describe more than the next stage of semiconductor development. Together, they illustrate a fundamental shift in how innovation is created across the industry. Progress is no longer defined solely by transistor scaling or a single breakthrough in manufacturing technology. Competitive advantage increasingly emerges from the integration of advanced process technologies, heterogeneous architectures, high-bandwidth memory, sophisticated packaging and resilient global supply chains. Artificial intelligence sits at the centre of this transformation. Its growing computational demands are reshaping processor design, accelerating the transition to advanced manufacturing nodes, driving demand for next-generation memory and making advanced packaging a strategic technology in its own right. At the same time, geopolitical priorities are influencing where these technologies are developed, manufactured and deployed. Viewed together, these trends point to a new model of semiconductor innovation. Future leadership will depend less on excellence in any single discipline and more on the ability to combine manufacturing, architecture, memory, packaging and software into highly integrated computing platforms. The next era of semiconductors will not be defined by one technology alone. It will be shaped by how successfully the entire ecosystem evolves as a unified engineering system. This article is based primarily on the TechInsights Semiconductor Outlook 2026 report, complemented by additional industry data and publicly available information.

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