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  • The Automotive Semiconductor Will Be Powered By These Next-Gen Automakers

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    Automotive Semiconductor

    Over the last two decades, the global automotive industry has undergone a fundamental transformation. A new class of automakers, established in the context of accelerating electrification, autonomy, and software-defined mobility, has emerged to challenge long-standing paradigms in vehicle design, manufacturing, and systems integration.

    Unlike their legacy counterparts rooted in internal combustion and mechanical engineering, these companies were founded when semiconductors had become central to the vehicle’s core functionality.

    From powertrain electrification and real-time sensor fusion to functional safety systems and edge computing for autonomous decision-making, modern cars have evolved into highly integrated, compute-intensive platforms.

    This shift places semiconductors at the center of vehicle architecture, governing performance, user experience, system reliability, and over-the-air serviceability. In this new automotive landscape, chip architectures, embedded software, and electronic subsystems are now as critical to competitive differentiation as the engine or chassis once were.


    Why Focus On New-Age Automakers?

    Traditional carmakers are adapting to EVs and autonomy, but newer players are born-native to software, electric powertrains, and platform thinking. Many of these companies began with the core assumption that:

    • Cars will be electric (or will use alternate fuel)
    • Driving will be assisted or autonomous
    • User experience will be digitally driven
    • Supply chains will increasingly revolve around silicon

    Their clean-sheet approach often puts them ahead on integration, over-the-air (OTA) updates, and leveraging high-performance chips. All while increasing the user experience.


    New-Age Automakers Since 2005

    In the past two decades, numerous new automotive manufacturers have emerged worldwide. Below is a comprehensive summary of these “new-age” automakers founded in 2005, covering passenger cars, commercial vehicles, and niche manufacturers. Each entry lists the company’s name, founding year, country of origin, market size (latest production volumes or valuation), target market segment, and vehicle type (EV, hybrid, etc.).


    North America (United States And Canada)

    The North American automotive sector has seen a wave of new companies born into the era of electrification, autonomy, and software-defined vehicles. These firms are redefining vehicle architecture and accelerating semiconductor integration across segments from luxury EVs to commercial platforms.

    NameFoundedCountryMarket Size (Latest Production / Valuation)Target MarketVehicle Type
    Lucid Motors2007 (as Atieva)USA~8,428 vehicles produced in 2023; ~6,000 delivered in 2023, market cap ≈ $7.7 B.Luxury sedans (Lucid Air); upcoming SUV (Gravity).EV (Battery Electric)
    Rivian2009USA57,000 EVs produced / 50,000+ delivered in 2023; market cap ~$12 B.Pickup trucks (R1T), SUVs (R1S), and delivery vans.EV (Battery Electric)
    Fisker Inc.2016USA~65,000 reservations; production began in 2023. Magna plant capacity ~120k/yr.Premium SUVs (Ocean), compact EV (PEAR).EV (Battery Electric)
    Nikola Motor2014USA209 electric trucks delivered by end of 2023; market cap <$1 B.Heavy-duty trucks (Tre semi tractor).Hydrogen Fuel-Cell & Battery EV
    Canoo2017USANo mass production yet; Walmart ~4,500 vans ordered. Market cap ~$0.3 B.Delivery vans, lifestyle vans, pickup trucks.EV (Battery Electric)
    Faraday Future2014USAFF 91 deliveries began in 2023. Valuation declined from $3.4 B via SPAC.Ultra-luxury SUVs (FF 91); future FF 81, FF 71.EV (Battery Electric)
    Bollinger Motors2014USAPre-production stage; acquired by Mullen Automotive in 2022.Off-road trucks and commercial truck chassis.EV (Battery Electric)
    Aptera Motors2006 (revived 2019)USAThousands of reservations for solar EV; no products delivered yet.Solar-powered three-wheeler commuter vehicle.EV (Battery Electric + Solar)
    Workhorse Group2007 (as AMP)USADelivered electric vans by 2020; low-scale operations ongoing.Delivery vans and utility drones.EV (Battery Electric)
    Lion Electric2008Canada~550+ electric buses/trucks as of 2022; listed with ~$1.4 B valuation (2021).Electric school buses and medium-duty trucks.EV (Battery Electric)
    Karma Automotive2014USAEstimated revenue of $213.3M as of 2025.Ultra-luxury sedans, coupes, and touring vehicles.EREV & EV (Battery Electric)

    Sources: Information was compiled from a range of credible industry and news outlets, including Electrek, Lucid Motors Investor Relations, Companies Market Cap, Wikipedia, Electric Drives, Rivian Stories, Futurride, Reuters, Magna, CleanTechnica, Bloomberg, PR Newswire, Enterprise League, Driving Vision News, Faraday Future’s Medium blog, Techpoint Africa, TechCrunch, and Medium.


    Europe

    Europe has emerged as a hub for next-generation automotive startups, with companies targeting electric hypercars, solar mobility, and urban transport. These firms are advancing new powertrain architectures, modular manufacturing, and software-driven design across diverse market segments.

    NameFoundedCountryMarket Size (Latest Production / Valuation)Target MarketVehicle Type
    Rimac Automobili2009Croatia~150 Nevera hypercars; valuation ~$2B post Porsche investment (2022).Hypercars; EV powertrains for OEMs.EV (Battery Electric)
    Arrival2015UKIPO valuation ~$13B (2021), dropped to <$10M (2023); assets sold to Canoo.Urban vans and buses via modular microfactories.EV (Battery Electric)
    Polestar2017Sweden / China~51,500 EVs delivered in 2022; ~$6–10 B market cap (SPAC 2022).Premium sedans, SUVs.EV (Battery Electric, PHEV)
    Automobili Pininfarina2018Italy~150 Battista hypercars planned; backed by Mahindra & Mahindra.Luxury electric hypercars.EV (Battery Electric)
    Lightyear2016NetherlandsBuilt Lightyear 0 prototype; insolvency in 2023; rebooting Lightyear 2.Solar-assisted long-range sedans.EV (Battery Electric + Solar)
    Sono Motors2016GermanyDeveloped Sion solar EV; project canceled in 2023, pivoted to retrofits.Compact solar family cars (discontinued).EV (Battery Electric)
    Volta Trucks2019UK / SwedenPre-production of 16-ton electric trucks; funding and production hurdles.Medium-duty logistics trucks.EV (Battery Electric)
    Microlino (Micro)2015Switzerland~150+ units delivered (2022–23); small-scale production in Italy.City microcars (2-seater bubble cars).EV (Battery Electric)

    Sources: Data compiled from Just Auto, Volvo Cars, Electronomous, TechCrunch, TechXplore, and MarkLines.


    Asia – China

    China has rapidly become a global epicenter for electric vehicle innovation, driven by a wave of startups founded in the last decade. These companies prioritize smart features, autonomous driving capabilities, and localized supply chains. Several have already reached significant production scale and are expanding globally, positioning China as a leader in next generation mobility.

    NameFoundedCountryMarket Size (Production / Valuation)Target MarketVehicle Type
    NIO Inc.2014China160,000 EVs delivered in 2023; ~450k total by end of 2023.Premium SUVs and sedans; focus on tech and battery swapping.EV (Battery Electric)
    Xpeng Motors2014China120,757 EVs in 2022; ~66k in 2023 (dip); rebound expected in 2024.Smart sedans and SUVs; focus on in-house ADAS (XPILOT).EV (Battery Electric)
    Li Auto2015China376,000 vehicles delivered in 2023; fastest-growing Chinese EV startup.Extended-range electric SUVs (EREV); family focused.EREV (Extended-Range EV)
    Weltmeister (WM)2015China~100k total units by 2022; entered bankruptcy in 2023.Affordable crossovers and sedans for mass market.EV (Battery Electric)
    Leapmotor2015China~111,000 EVs in 2022; entering European markets in 2023.Budget EVs including city cars, coupes, and mid-size sedans.EV (Battery Electric)
    Hozon Auto (Nezha)2014China~152,000 units in 2022; continued growth in ASEAN region.Entry-level and mid-range EVs with good value proposition.EV (Battery Electric)
    Human Horizons2017ChinaThousands of HiPhi X units sold since 2020; priced ~$100k+.Tech-heavy luxury EVs; competing with premium global brands.EV (Battery Electric)
    Byton (defunct)2016ChinaRaised ~$1.2B; never launched. Ceased operations in 2021.Smart luxury SUVs and sedans; failed to commercialize.EV (Battery Electric)
    BYD Auto2003ChinaOver 3 million vehicles sold in 2023; market cap > $80B.Full spectrum: passenger cars, luxury, buses, commercial.EV (Battery Electric), PHEV
    MG Motor (SAIC)2007 (SAIC acquisition)ChinaOver 800,000 vehicles sold in 2023 globally.Affordable EVs and hybrids for global markets.EV (Battery Electric), Hybrid
    Aion (GAC Group)2017China480,000+ vehicles sold in 2023.Mid-range and premium EVs (Aion Y, Aion LX, Hyper GT).EV (Battery Electric)
    Zeekr (Geely Group)2021ChinaOver 160,000 vehicles sold in 2023.Premium performance EVs (Zeekr 001, Zeekr X, 009 MPV).EV (Battery Electric)
    Avatr (Changan + CATL + Huawei)2021ChinaEarly stage; deliveries ramping in 2023.High-end smart EVs with Huawei tech integration.EV (Battery Electric)
    IM Motors (SAIC + Alibaba)2020ChinaLow volume; deliveries began in 2022.Tech-driven luxury EVs (IM L7, LS7).EV (Battery Electric)
    Rising Auto (SAIC)2021ChinaRebranded from SAIC’s Roewe line; early growth phase.Smart electric sedans and crossovers.EV (Battery Electric)
    Seres (AITO, Huawei JV)2016ChinaPartnered with Huawei; launched M5 and M7 models.Hybrid and EV SUVs integrated with Huawei ecosystem.EV (Battery Electric), EREV

    Sources: Data from CNEVPost, CarNewsChina, Technode, Reuters, TechCrunch, XPeng, and company investor portals.


    Asia – Other Regions (India, Southeast Asia, Middle East, Africa)

    Across India, Southeast Asia, the Middle East, and Africa, new automotive companies have emerged, addressing local market needs with electric mobility, affordability, and regional manufacturing. These companies range from high-volume e-scooter producers and electric bus makers to ultra-luxury and government-backed initiatives. Together, they represent a critical extension of the global shift toward sustainable and localized automotive innovation.

    NameFoundedCountryMarket Size (Production / Valuation)Target MarketVehicle Type
    VinFast2017Vietnam~35,000 vehicles sold in 2023; market cap dropped from $85B to ~$6B.Mass-market EVs (VF8, VF9); global expansion plans.EV (Battery Electric)
    Togg2018Turkey28,000 orders in first sales day (2023); scaling to 100k/year by 2025.Electric SUVs and upcoming sedan/hatchback models.EV (Battery Electric)
    Ola Electric2017India~150,000 e-scooters sold (2022–23); electric car in pipeline.Two-wheelers and future budget EV hatch for India.EV (Battery Electric)
    Pravaig Dynamics2011IndiaPrototype sedan launched; SUV under development for 2024.Luxury EVs for Indian market; premium focus.EV (Battery Electric)
    Mobius Motors2010KenyaDozens of Mobius II SUVs built since 2015; halted in 2023.Low-cost rugged SUVs for African roads.Gasoline (ICE)
    Kiira Motors2014UgandaBuilding electric buses; scaling to 1,000+ units/year.City buses and hybrids for East Africa.EV & Hybrid
    Innoson Vehicle Mfg2007NigeriaAssembles a few thousand vehicles yearly; Nigerian govt support.Low-cost cars, buses, pickups for West Africa.Gasoline (ICE)
    Ceer2022Saudi ArabiaFirst EVs expected in 2025; JV with Foxconn; BMW platform licensed.EV sedans and SUVs for GCC and Middle East.EV (Battery Electric)
    W Motors2012UAE/LebanonLimited-run hypercars; expanding into electric/autonomous prototypes.Ultra-luxury sports cars; now exploring EV-based law enforcement vehicles.Gasoline (ICE), EV planned

    Sources: Data from Just Auto, Reuters, Techxplore, Medium, MarkLines, Dailysabah, Tribune PK, Wikipedia, and official company releases.


    What This New-Age Automakers Mean For The Automotive Semiconductor Industry

    This new generation of automakers is not merely a business disruption. A structural shift intensifies the demand for semiconductors across the automotive stack. A modern vehicle today, integrates more than 100 different types of silicon solutions, and this number is set to grow as autonomy, connectivity, and software-defined features expand.

    What matters is not just chip volume but compute density and architectural dependency. ADAS, infotainment, and battery systems now rely on advanced SoCs and specialized silicon with performance levels approaching data centers.

    Key implications for the semiconductor industry:

    • Co-development is becoming standard as automakers involve chipmakers early in the design process
    • Domain-specific architectures are evolving faster by leveraging innovations from AI, mobile, and high-performance computing
    • Power electronics, safety systems, and battery management require integrated, real-time, capable solutions
    • Regional supply chains and custom silicon initiatives are gaining momentum in strategic market

    As new automakers scale, semiconductor companies must align closely with their timelines, architectures, and localized production strategies. Success will depend on delivering optimized computing and resilient system design.

    Thus, semiconductors are no longer peripheral to vehicles. They are now the foundation of performance, safety, and product differentiation.


  • Cost Challenges Of Getting Advanced Semiconductor Products To Market

    Published By: Electronics Product Design And Test
    Date: May 2025
    Media Type: Online Media Website And Digital Magazine

  • Cost Challenges Of Getting Advanced Semiconductor Products To Market

    Published By: Electronics Product Design And Test
    Date: May 2025
    Media Type: Online Media Website And Digital Magazine

  • The Semiconductor DFT Approach That Shapes IC Reliability

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    How DFT Evolved Beyond Test To Impact Reliability

    In the early years of integrated circuit (IC) design, Design-for-Testability (DFT) was primarily introduced to improve manufacturing fault coverage and lower production test costs. Techniques such as scan chains, Built-In Self-Test (BIST), and boundary scans were developed to ensure that devices could be tested efficiently after fabrication. The goal was straightforward: detect manufacturing defects like stuck-at faults or shorts and maximize the number of good parts shipped.

    Reliability, however, existed in a separate part of the development cycle. It focused on burn-in testing, life testing, and field failure analysis; activities typically performed long after the design phase had concluded. Early semiconductor technologies, with larger geometries and lower integration density, were far more tolerant of marginalities, allowing this separation between test and reliability efforts to function without significant consequences.

    However, as the industry pushed into smaller nodes and began designing chips for automotive, medical, and aerospace applications, latent defects and marginal circuit behaviors became much harder to contain. The traditional DFT focus, catching only complex manufacturing faults, was insufficient. Subtle weaknesses introduced during fabrication could evolve into catastrophic failures after months or years of use in harsh real-world environments.

    It became increasingly clear that DFT had to evolve. It was no longer just about passing production tests. It had to become a tool for reliability assurance, enabling early detection of life-limiting defects, supporting real-time health monitoring, and even allowing mechanisms for post-silicon repair.

    From my experience, companies that recognized and embraced this expanded view of DFT, starting in the late 1990s and early 2000s, saw dramatic reductions in field returns and warranty failures, giving them a lasting advantage in high-reliability markets.


    Techniques That Make DFT A Reliability Enabler

    Modern Design-for-Testability (DFT) practices have evolved beyond providing basic test access. Today, DFT intentionally embeds structures and strategies directly contributing to early failure detection, ongoing health monitoring, and long-term reliability assurance. Some of the key techniques that have reshaped DFT’s role include:

    Margin-Aware Testing: Contemporary DFT architectures are designed to detect functional faults and uncover marginal timing vulnerabilities. Techniques such as path delay fault testing, dynamic timing analysis, and voltage and temperature corner testing are now integrated into scan methodologies. These approaches help expose subtle risks like race conditions, timing slippage, and setup/hold margin failures that might otherwise surface only after prolonged field operation or under environmental stress.

    Embedded Health Monitors: Modern ICs now embed a range of on-chip monitors to track critical reliability parameters in real-time. These include thermal sensors, voltage droop detectors, electromigration stress monitors, and aging sensors based on phenomena such as BTI (Bias Temperature Instability) and HCI (Hot Carrier Injection). By continuously observing these degradation mechanisms, the system can identify early warning signs of device wear-out before traditional end-of-life testing catches them.

    Built-In Self-Repair (BISR): While BISR originated in memory arrays to allow the replacement of faulty rows or columns, its philosophy has expanded. Logic BISR concepts are now used to incorporate spare functional blocks, redundant paths, or self-reconfigurable circuits. These enable post-manufacture defect mitigation and even in-field dynamic recovery, which is necessary for high-availability and mission-critical applications like autonomous driving and aerospace systems.

    Accelerated Degradation Detection: Instead of relying solely on lengthy burn-in processes, modern DFT includes stress-inducing scan patterns and high-activity test sequences designed to accelerate latent defect manifestation. Techniques such as elevated voltage toggling, thermal cycling stress patterns, and high-frequency clock strobing allow manufacturers to screen out devices at higher risk of early-life failure during final tests, significantly reducing the “infant mortality” tail in reliability distributions.

    Each technique transforms DFT from a purely manufacturing-oriented tool into a cornerstone of predictive reliability engineering. In my direct experience across multiple technology nodes, products that integrated these advanced DFT capabilities consistently achieved twice the mission life compared to similar designs that treated DFT as a late-stage add-on.

    The lesson is clear: DFT, designed with reliability in mind, becomes a silent but critical insurance policy for every IC leaving the factory.


    Lessons Learned From Real-World Failures

    There is no substitute for experience, especially the hard kind. In the semiconductor industry, field failures often reveal gaps that qualification testing alone cannot uncover. A standard failure mode seen across technologies, particularly in mission-critical applications, involves minor timing shifts and voltage droop effects not captured by nominal-condition scan testing.

    These subtle issues may pass initial qualification yet surface under extreme environmental stresses, such as cold starts or wide voltage variations.

    These cases highlight a critical truth: reliability-driven DFT must extend beyond validating basic functionality. It must be architected to validate timing margins, stress responses, and full-system robustness under real-world operating extremes. Without a margin-aware, environment-sensitive approach, latent vulnerabilities can remain hidden until the device is in the field, leading to costly returns, warranty claims, and potential safety risks.

    Modern best practices now mandate that DFT strategies include corner-aware testing across full environmental ranges, embedded degradation monitors for voltage, temperature, and electromigration, and qualification-resilient test logic that does not become a new failure source itself.

    DFT is no longer viewed as a mere checklist item or manufacturing tool. It is a fundamental mindset shift, treating every test structure and validation point as an active contributor to long-term product reliability and customer trust.


    Best Practices To Align DFT And Reliability

    Specific patterns have become clear after decades of trial and error and technical evolution. Teams that successfully use Design-for-Testability (DFT) to enhance IC reliability follow a deliberate and disciplined approach that starts early, embeds margin awareness, and treats DFT as an investment, not a burden.

    Below is a summary of the best practices that consistently deliver results across complex and mission-critical applications.

    PracticeKey Focus
    Start EarlyIntegrate DFT and reliability engineering during architectural planning, not after layout completion.
    Test Margins, Not Just LogicValidate path delays, power integrity, and signal integrity margins using dedicated DFT hooks.
    Embed Monitors ThoughtfullyPlace thermal sensors, voltage droop detectors, and electromigration monitors strategically at critical locations.
    Plan For In-Field VisibilityArchitect DFT structures that enable monitoring during system operation, not just at manufacturing test.
    Stress Test IntelligentlyUse stress-inducing scan patterns and built-in stress circuits to detect infant mortality risks early.
    Treat DFT As A Reliability AssetShift mindset: view DFT as an insurance policy against field failures and warranty costs, not as overhead.

    As ICs move deeper into critical applications, from autonomous vehicles to implantable medical devices and AI accelerators, the relationship between DFT and reliability will no longer be a luxury or competitive advantage; it will be necessary for survival.

    Those who design with this mindset will not only ship better silicon, they will build trust, longevity, and leadership in industries where failure is not an option.


  • The Semiconductor World Still Runs On Older Nodes

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    How Large Is The Mature Node Economy

    The focus immediately shifts to cutting-edge nodes such as 3nm or 5nm when discussing semiconductors. However, the economic and technological importance of mature semiconductor processes, typically those at or above the 28nm (or 40nm in many cases) node, remains substantial and essential to the industry’s foundation.

    Key technical and economic advantages of mature nodes:

    • Cost Efficiency and Equipment Depreciation: Mature semiconductor processes utilize fully depreciated equipment, dramatically lowering capital investment and operational costs compared to advanced-node manufacturing.
    • High Yield and Process Stability: Due to extensive operational experience, mature nodes achieve consistently high yields. Process maturity and thoroughly characterized manufacturing steps significantly enhance productivity and reduce variability.
    • Established IP and Rapid Qualification: Mature nodes feature extensive intellectual property (IP) libraries and proven design ecosystems, facilitating faster qualification, shorter design cycles, and more predictable product ramps.

    These legacy nodes form the critical backbone across numerous essential industries. They drive analog integrated circuits (ICs), power management ICs, automotive microcontrollers, display driver ICs, embedded non-volatile memory solutions, and various sensor-based applications. From a financial standpoint, mature nodes generate a robust, multi-billion-dollar revenue stream, providing economic stability and supporting foundational technologies critical to numerous global industries.


    Technical Sweet Spots That Keep Older Nodes Relevant

    Mature semiconductor nodes possess distinct technical strengths that make them uniquely valuable. They provide specialized features that are challenging or costly for advanced nodes to replicate. Automotive microcontrollers, motor control ICs, industrial controllers, and battery management systems frequently achieve optimal performance within planar CMOS nodes between 40 and 90 nanometers.

    Globally, mature-node manufacturing also continues to represent more than half of total wafer output, dominating 200 mm (eight-inch) and 300 mm (twelve-inch) wafer fabs across semiconductor hubs in Taiwan, the United States, Europe, and Asia.

    These mature technologies offer robust embedded nonvolatile memory capabilities, delivering high-speed access and extended data retention, which are critical attributes in demanding automotive and industrial environments. Additionally, they leverage thick-oxide transistor designs, which comfortably support voltages above 60 volts, enabling reliable operation in power management and motor control circuits.

    Precision analog front-end circuits are another strong suit for older nodes, benefiting from inherently lower noise characteristics and superior linearity thanks to larger transistor dimensions. Integrating these analog functions alongside substantial nonvolatile memory on a single chip significantly reduces complexity and cost, particularly when incorporating similar functionalities within advanced FinFET-based nodes.

    Together, these technical advantages solidify mature nodes as the optimal choice for specific use cases where reliability, analog precision, high-voltage handling, and cost efficiency are paramount.


    Fresh Money Flows Into Legacy Capacity

    Investment and capacity expansion in mature semiconductor nodes are not merely ongoing. They are accelerating significantly. Across the industry, foundries are rapidly scaling their mature-node manufacturing capabilities, with expansions frequently adding tens of thousands of wafers per month to existing facilities. For instance, one prominent foundry is boosting its 28nm, targeting robust demand from the automotive, industrial, and consumer electronics sectors.

    At the same time, governments worldwide recognize the strategic importance of mature nodes, resulting in significant financial support. For example, recent government initiatives include a commitment of more than a billion dollar to enhance domestic mature-node manufacturing in the United States, explicitly aiming to bolster capabilities critical for the automotive, industrial, defense, and aerospace sectors.

    Similar expansions globally reinforce this trend. European governments have initiated strategic investments in fabs operating between 22 and 180nm nodes to strengthen regional supply chains and ensure technological sovereignty. Meanwhile, joint ventures across the globe (mainly in Asia and EU) are significantly increasing capacity at nodes such as 90nm and 180nm to meet the growing demand for analog and power-management ICs.


    Strategic Outlook For Engineers, Investors, And Policy Makers

    Older semiconductor nodes are far from obsolete. They represent highly optimized platforms meticulously refined through decades of production experience. These mature nodes deliver exceptional reliability, predictable yield performance, and proven operational stability.

    Their inherent cost efficiency, driven by fully depreciated equipment and mature manufacturing processes, makes them economically compelling. Additionally, specialized performance characteristics such as high-voltage handling, precision analog integration, robust embedded memory solutions, and radiation tolerance make older nodes indispensable for specific market segments, including automotive, industrial, and aerospace.

    A summarized strategic perspective:

    StakeholderStrategic Importance
    EngineersMature nodes provide trusted solutions for analog precision, high-voltage capabilities, radiation tolerance, and embedded non-volatile memory. Extensive IP reuse accelerates design timelines.
    InvestorsFully depreciated fabs with predictable, stable demand offer attractive margins. Investments have lower risk profiles due to established processes and equipment.
    Policy MakersMature semiconductor processes are strategically essential for national security, automotive resilience, and economic stability. Policy frameworks increasingly treat legacy semiconductor manufacturing as critical infrastructure rather than commodity production.

    Collectively, these strategic considerations confirm that older semiconductor nodes will maintain their critical role well into the next decade, even as the semiconductor industry’s leading edge continues to advance toward ever-smaller technology nodes.


  • The Total Cost Of Ownership In Semiconductor Business

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    What Is Total Cost of Ownership (TCO)?

    In the semiconductor industry, the cost of a tool, IP block, or software license is rarely limited to what appears on the purchase order. That figure is only the beginning of the financial story.

    Total Cost of Ownership, or TCO, is a structured approach that enables companies to assess the full economic impact of acquiring, operating, and maintaining a product or service across its entire working life.

    It encompasses the following:

    Initial Purchase Cost: The upfront investment required to acquire the asset. This is often the most visible, yet frequently the smallest portion of the overall cost

    Operational Costs: This includes recurring expenditures such as power, cleanroom real estate, workforce, license renewals, and consumables. These are day-to-day costs that quietly accumulate

    Maintenance And Support: Over time, service contracts, spare parts, calibration routines, software patches, and staff training become essential to sustained performance

    Downtime And Productivity Losses: Every hour of tool unavailability or design team obstruction, often caused by bugs, delays, or compatibility issues, translates directly into lost revenue and time-to-market pressure

    End-of-Life Costs: When a system is retired, further investment may be required for decommissioning, migrating to newer technologies, or adapting legacy workflows

    As the semiconductor business is continuously operated at the intersection of capital intensity and precision, a decision that reduces cost by a few percentage points can easily result in millions in hidden losses if it compromises reliability, throughput, or product quality.

    Consider this:

    • A lower-cost tester that lacks precise thermal control may undermine test integrity, leading to field reliability issues and customer dissatisfaction
    • A less expensive IP block may have limited support or outdated documentation, resulting in costly silicon re-spin and substantial schedule delays
    • TCO encourages a shift in thinking from a short-term view focused on cost minimization to a longer-term view centered on operational value

    In the end, TCO is not just a financial metric. It is a discipline that helps teams make more efficient.


    TCO In Different Parts Of The Semiconductor Business

    Every segment of the semiconductor value chain, whether in fabrication, testing, design, or packaging, carries its own distinct Total Cost of Ownership profile. Each function introduces a unique set of variables that influence long-term cost. Understanding how TCO manifests across these areas is not just a matter of accounting accuracy.

    Let us break it down by function:

    SegmentInitial CostOperational CostSupport & MaintenanceDowntime/Hidden Cost
    FAB EquipmentCapital investment in tools like lithography, etch, depositionUtilities, cleanroom usage, consumables like gas and chemicalsCalibration, spare parts, OEM support, software updatesYield loss, WIP delays, throughput impact due to tool unavailability
    Test EquipmentATEs, handlers, probers, loadboardsPower, thermal systems, test time per unitSocket wear, handler maintenance, debug supportMissed shipments, higher cost of quality, yield loss from mechanical issues
    EDA Tools And IPTool licenses, IP block purchase feesCompute infrastructure, integration effortTool support, version updates, bug fixesProject delays, silicon re-spins due to integration/debug issues
    Materials And ConsumablesPer-unit material cost (e.g., photoresist, leadframes, substrates)Volume-driven spend, contamination risk, rework impactIncreased tool cleaning, wear and tearLower yield, instability, latent defects
    Facility And InfrastructureHVAC, power systems, cleanroom buildoutElectricity, water, gas supply for continuous operationsFilter replacements, backup systems, emergency repairsProduction disruptions, scalability limits during expansion

    It is essential to avoid costly surprises, manage operational risk, and make informed, future-focused investment decisions. Over time, this perspective often separates companies that scale efficiently from those that struggle to contain hidden losses.


    Hypothetical Examples And Mistakes To Avoid

    Let us consider few hypothetical examples to explain TCO.

    Example 1: To reduce capital expenses, a semiconductor firm selected a low-cost test handler for high-volume automotive lines. However, the handler underperformed thermally in production, leading to a 4 to 6 percent yield drop and multiple customer quality issues. The recovery costs far outweighed the initial savings, highlighting that long-term reliability matters more than price.

    Example 2: Another company reused an older IP block to avoid new licensing fees. The IP was incompatible with the current process node and poorly documented. Integration issues went undetected, resulting in a post-silicon bug and a costly response. The delay stretched timelines and added over three million dollars in rework.

    DecisionGoalUnseen Cost
    Low-cost test handlerSave CapExYield loss, quality issues
    IP reuseAvoid licensing feeSilicon respin, delays
    Budget EDA toolReduce license costEngineering inefficiency
    Used fab toolSave equipment costIncreased downtime

    Example 3: A design team switched to a cheaper simulation tool to cut license costs. However, the tool was unstable, with slow runtimes and limited support. Engineers lost valuable time managing tool issues, leading to delays and lowered team efficiency. The short-term savings came at the cost of long-term productivity.

    Example 4: A fabrication facility bought a used etch tool to reduce capital investment. While initially functional, it lacked software updates and required frequent maintenance. Uptime suffered, disrupting wafer cycle times and impacting line stability. The operational drag soon eclipsed the upfront benefit.

    These cases show that decisions to save money upfront can introduce hidden costs in quality, time, and yield. TCO helps teams evaluate the full financial impact beyond the purchase price.


    Integrate TCO Thinking Into Engineering And Business Decisions

    TCO is more than a finance metric. It is a way of planning that must be built into engineering, procurement, and operational decisions. For engineers, this means looking beyond technical specs and considering long-term impacts such as debug effort, uptime, integration complexity, and reusability. Asking what might go wrong after deployment often reveals the fundamental cost drivers.

    Procurement teams should work closely with engineering to move beyond basic quotes. They must evaluate uptime history, support terms, maintenance cycles, and parts availability. Eventually, two tools with similar specs can vary widely in lifetime cost due to serviceability and consumable usage.

    WhoAction For TCO Thinking
    EngineersConsider long-term debug, yield, and integration risks
    ProcurementEvaluate beyond price: uptime, service, and lifecycle support
    Business leadersUse 3–5 year TCO models in planning and ROI analysis
    Cross-functional teamsShare lessons learned and maintain internal TCO benchmarks

    Business leaders can use three to five-year TCO models to improve cost forecasting and ROI decisions. For example, a lower-cost tester may reduce CapEx but limit throughput, while a higher-end tool may improve unit economics in volume production. Planning with this view leads to more resilient product execution.

    Finally, TCO thinking must be shared across functions. Engineering, finance, quality, and operations should jointly define benchmarks and track performance over time. Reviewing past decisions helps organizations avoid repeating costly oversights.


  • The Semiconductor Smart Factory Basics

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    What Is A Semiconductor Smart Factory?

    If you have spent time reading developments in the semiconductor industry, you have probably heard about the “Semiconductor Smart Factory” term. But what exactly does that mean?

    In simple terms, a Semiconductor Smart Factory is like your regular manufacturing site, just a bit smarter. Think of a conventional semiconductor manufacturing (FAB or OSAT) plant, it is already pretty impressive, equipped with complex machines, operators in clean-room suits, and sophisticated processes designed to produce microscopic chips that power different applications.

    But here is the twist: in a smart factory, the whole operation gets digitally interconnected, powered by advanced technologies such as real-time data analytics, artificial intelligence (AI), robotics, and extensive sensor networks.

    Now, imagine this scenario: instead of manually keeping tabs on every piece of equipment, a smart factory has sensors tracking machine health and wafer quality in real-time. These sensors communicate continuously with powerful AI systems, analyzing mountains of data instantly to predict equipment failures before they occur, optimize production scheduling on the fly, and automatically adjust processes to minimize defects and maximize yield. It is like giving your entire manufacturing line a brain and eyes, empowering it to make smart decisions independently, while humans oversee and fine-tune operations from a control room.

    So, why does this matter now more than ever? With chip demand skyrocketing, thanks to trends like AI, automotive electrification, and emerging computing grids, the industry faces intense pressure to produce faster, cheaper, and at a higher quality. Traditional semiconductor manufacturing sites simply cannot keep pace anymore. That is where semiconductor smart factories step in, providing a path toward better efficiency, reduced costs, and increased flexibility.

    Bottom line: smart factories are not just a trendy buzzword. They are becoming the backbone for semiconductor manufacturing, transforming the way the industry operates, competes, and innovates.


    Key Components Of A Semiconductor Smart Factory

    Building a semiconductor smart factory is not just about developing a facility with flashy robots or AI-driven tools. It is about strategically integrating technology that fundamentally changes the way a factory works, transforming raw data into actionable decisions all by utilizing smart equipment.

    To make this happen, several key components need to come together seamlessly. Let us simplify these components and understand their roles clearly:

    ComponentWhat It DoesWhy It Matters
    Sensor NetworksCollects real-time data from equipment and environment.Immediate issue detection; reduces downtime.
    AI And Data AnalyticsProcesses data to predict issues and optimize decisions.Boosts yield; proactive rather than reactive.
    Automated MES (Manufacturing Execution System)Manages and automates factory operations digitally.Improves efficiency and traceability of wafers.
    Robotics And Autonomous Material HandlingAutomates wafer handling, transport, and processing.Minimizes contamination risks; enhances throughput.
    Digital Twins And SimulationVirtual modeling of processes and equipment.Enables safer testing, optimization, and innovation.
    Cybersecurity InfrastructureProtects interconnected systems and data streams.Ensures safe operation and protects intellectual property.

    By bringing these elements together, a Semiconductor Smart Factory achieves the crucial balance between productivity, quality, flexibility, and security. It is not about blindly adopting technology but thoughtfully selecting and integrating these systems so they enhance and complement each other.

    Ultimately, these components form the backbone of modern semiconductor manufacturing, preparing factories not just to meet today’s demand but to stay ahead of tomorrow’s challenges.


    Benefits Of Implementing A Smart Factory Approach In Semiconductor Industry

    When semiconductor companies consider moving toward smart factories, they are not just chasing tech trend. The goal is to have tangible, practical benefits that boost factory performance and profits.

    First up is improved yield and productivity. Traditional semiconductor manufacturing often face costly downtime or wafer defects from minor issues. Smart factories use real-time analytics and AI to predict and prevent these problems before they occur, ensuring higher yields and steady production.

    Predictive maintenance is another key advantage. Instead of reacting after equipment breaks, smart factories anticipate failures ahead of time. This proactive approach significantly cuts downtime and saves money. Then there is traceability. Smart factories digitally track every wafer, tool, and process step in real-time. This transparency speeds up troubleshooting, boosts product quality, and strengthens customer trust.

    Additionally, smart factories offer unmatched flexibility. They quickly adapt to changing market demands, scaling production and integrating new processes smoothly, essential for staying competitive. Lastly, sustainability improves dramatically. Data-driven control means reduced waste, optimized energy usage, and a greener manufacturing footprint.

    In short, semiconductor smart factories deliver increased yield, reduced downtime, enhanced flexibility, and better sustainability, all essential for staying ahead in today’s competitive market.


    Takeaway

    Transitioning to a Semiconductor Smart Factory model is no longer optional, it is becoming essential for semiconductor companies aiming to stay competitive. Smart factories directly boost yield, reduce operational costs through proactive maintenance, and enhance production flexibility.

    Although initial investments can be substantial, long-term savings from reduced downtime, lower defect rates, and optimized energy consumption significantly offset these costs.

    By strategically balancing upfront expenses with future efficiency gains, manufacturers ensure they are not just keeping pace but staying ahead in today’s fiercely competitive semiconductor market.


  • Factors Affecting Semiconductor Node Selection

    Published By: Electronics Product Design And Test
    Date: April 2025
    Media Type: Online Media Website And Digital Magazine

  • The Future Of Semiconductor Design As Open Source Is Real Alternative Or Just Wishful Thinking

    Image Generated Using DALL-E


    The Rise Of Open-Source Semiconductor Design

    Traditionally, semiconductor design has been a highly proprietary field dominated by closed ecosystems and tightly controlled intellectual property frameworks. Major industry players have historically maintained strict control over processor architectures, electronic design automation (EDA) tools, design methodologies, and fabrication processes.

    While this model delivered consistent innovation and robust technology roadmaps, it also created significant barriers to entry due to costly licensing agreements, complex proprietary toolchains, and limited transparency into underlying technologies.

    However, a compelling shift toward open-source semiconductor design has begun reshaping this paradigm in recent years. Central to this movement is the rise of open Instruction Set Architectures (ISAs). Most notably, RISC-V, a modular, extensible, and license-free ISA, was initially developed within academic research environments.

    Unlike traditional, proprietary ISAs, which require licensing fees and impose restrictions on customization, open-source architectures offer the semiconductor community the freedom to modify, enhance, and tailor processor cores and related subsystems to specific applications.

    In parallel, the emergence of open-source EDA tools and design flows. Such as openly available RTL-to-GDS toolchains and community-driven physical design kits (PDKs) have further accelerated this shift. Open-source EDA solutions democratize access by enabling a wider range of developers, startups, and research institutions to explore innovative chip designs without prohibitive upfront costs. Although still maturing compared to established proprietary platforms, these tools provide transparency, flexibility, and community-driven innovation.

    This combination of open ISAs, openly accessible EDA tools, and freely available design resources is gradually transforming semiconductor design from a closed, resource-intensive field to a collaborative and broadly accessible discipline.

    The implications are profound: Innovation can occur faster, experimentation can be more widespread, and participation from academia and smaller enterprises can flourish, ultimately fostering a more diverse and dynamic semiconductor ecosystem.


    Real Potential: Benefits and Success Stories

    Open-source semiconductor design brings tangible benefits and measurable real-world outcomes, reflecting its growing viability and potential.

    BenefitDescriptionExample or Success Story
    Customization and FlexibilityFreedom to customize open-source ISA cores, adding specialized instructions optimized for unique workloads.AI accelerators leveraging open ISA cores with custom vector instructions to enhance machine-learning workloads.
    Cost EfficiencySignificant reduction in licensing fees, enabling affordable access to EDA tools, IP cores, and design flows.Startups leveraging open-source RTL-to-GDS toolchains and publicly available PDKs to prototype chips affordably.
    Innovation AccelerationFaster design cycles enabled by community-driven contributions, collaborative design processes, and reusable IP blocks.Academic groups successfully implementing open-source microcontrollers into silicon prototypes within months, significantly shortening time-to-market.
    Enhanced Security And TransparencyFull transparency into hardware implementations facilitates improved security auditing, risk reduction, and reliability verification.Open-source cores widely adopted for safety-critical and security-focused embedded applications, where transparency enables rigorous verification.
    Community-Driven EcosystemActive global communities contribute code, documentation, testing, and knowledge sharing, accelerating development and adoption.Rapid ecosystem expansion, with community-led development of mature libraries, IP modules, and design automation scripts freely shared online.
    Real-World DeploymentOpen-source ISAs and EDA tools now actively deployed in commercial products, demonstrating production-level maturity and trustworthiness.Open-source microcontrollers widely adopted in IoT sensors and embedded systems in commercial deployments globally.


    Practical Limitations: Challenges and Hurdles

    While open-source semiconductor design offers substantial potential, practical challenges currently restrict its widespread adoption. Foremost among these is the maturity gap between open-source electronic design automation (EDA) tools and established proprietary counterparts. Open-source toolchains often lag in advanced features, comprehensive documentation, and extensive industry-standard verification flows.

    Additionally, open-source ecosystems struggle with consistency, especially around standardization of libraries, IP cores, and fabrication process compatibility, leading to reliability and repeatability concerns in mission-critical applications.

    Furthermore, intellectual property (IP) protection and governance present significant hurdles. Open-source semiconductor initiatives inherently raise questions about IP infringement risks, patent liabilities, and clear licensing terms. The absence of universally accepted governance models and IP frameworks creates hesitation among companies, particularly those serving automotive, aerospace, or healthcare sectors, where stringent reliability, security, and compliance requirements demand robust legal and operational clarity.

    Without addressing these critical IP and governance concerns, large-scale commercial adoption of open-source semiconductor designs will continue to face resistance and cautious scrutiny.


    Takeaway: Real Alternative Or Wishful Thinking

    Open-source semiconductor design is neither entirely wishful thinking nor a complete replacement for established proprietary solutions, rather, it represents a viable and increasingly impactful complement within the broader semiconductor landscape.

    The momentum behind open-source ISAs, EDA tools, and collaborative community projects demonstrates real-world applicability, particularly in innovation-driven fields like embedded systems, research prototyping, and academic exploration.

    However, challenges such as tool maturity, intellectual property governance, and standardization must be realistically addressed for broader industry acceptance. Ultimately, the future likely lies in a hybrid ecosystem, where open-source models coexist and integrate seamlessly with proprietary technologies, each leveraging their strengths.

    For open-source semiconductor design to become a mainstream alternative, continued investment in tooling, clear legal frameworks, and robust community-industry collaboration are essential. With these foundations, the promise of open-source semiconductor design can evolve beyond optimism into a sustained, practical reality.


  • The Role Of AI In Semiconductor Manufacturing: Fact Or Fiction

    Image Generated Using DALL-E


    The AI Debate

    Artificial Intelligence (AI) often sparks divided opinions as a groundbreaking innovation or technological hype.

    At the same time, in semiconductor manufacturing, where billions of dollars depend on minuscule yield and efficiency gains, the industry must critically evaluate whether AI delivers transformative results or is merely overblown. Semiconductor FABs and OSATs globally are already investing heavily in AI-driven solutions, leveraging predictive maintenance to reduce equipment downtime, AI-powered Automated Optical Inspection (AOI) to reliably detect subtle defects in packaging, and adaptive testing to reduce costs without compromising quality.

    Despite these promising outcomes, it is important to remain realistic. Claims of fully autonomous fabs or entirely self-driving manufacturing environments are exaggerated. While AI significantly enhances productivity and quality, semiconductor manufacturing relies fundamentally on skilled engineers to interpret AI insights, make strategic decisions, and integrate these technologies into existing systems. Thus, AI’s genuine value is clear, but only if deployed with measured expectations, careful validation, and thoughtful integration strategies.


    Is AI Integration A Necessity In Semiconductor Manufacturing?

    While labeling AI indispensable due to its popularity is tempting, a critical examination still reveals a nuanced picture. Semiconductor manufacturing thrived long before AI, achieving innovation through rigorous engineering, strict quality control, and methodical experimentation.

    Thus, it is fair to ask whether AI is necessary or merely another technological “nice-to-have”?

    Let Us Understand Why Skepticism Is Valid: AI is powerful but brings complexities, high integration costs, demanding data requirements, and organizational barriers. Traditional methods may remain sufficient and economically practical for fabs running mature or legacy processes (e.g., analog or 130nm+ nodes). Additionally, reliance on AI without adequate expertise or infrastructure can lead to confusion, causing AI-generated insights to be misunderstood and potentially harming operational efficiency.

    How AI Can Be Essential In Semiconductor Manufacturing: Despite valid skepticism, the necessity of AI becomes unmistakable when viewed through the lens of today’s leading-edge semiconductor processes. AI integration is becoming necessary due to the staggering complexity at advanced nodes (7nm, 5nm, 3nm, and beyond), complex packaging technologies, and the need for exact manufacturing tolerances.


    Cost Of Deploying AI In Semiconductor Manufacturing

    Deploying AI in semiconductor manufacturing offers substantial benefits, such as enhanced yield, reduced downtime, and improved efficiency. However, these advantages require significant upfront and ongoing investments. Costs depend heavily on fab size, technology node, and existing infrastructure.

    Infrastructure-related investments typically include powerful GPUs, specialized AI accelerators, cloud or edge computing, robust data storage, and networking infrastructure for real-time analytics. AI software licensing, often from commercial platforms or customized proprietary solutions, also represents a substantial cost component.

    Data preparation and integration also add notable expenses, as AI requires clean, labeled, and integrated data. Labor-intensive processes such as data labeling, cleaning, and system integration across MES, test equipment, and legacy infrastructure further increase costs.

    Cost ComponentEstimated Cost (USD)
    AI Hardware Infrastructure$500K – $2M
    AI Software Licensing And Tools$200K – $1M annually
    AI Data Integration And Preparation$200K – $500K
    AI Talent Acquisition And Training$300K – $1M annually
    Annual Maintenance And Operations Of AI$100K – $400K annually
    Total First-Year Costs~$1.3M – $4.9M
    Sources: Industry Reports

    Deploying AI also demands significant investment in talent acquisition and workforce training. Companies must hire specialized AI/ML engineers and data scientists,. Training for existing engineers and operational staff is also critical to ensure effective AI system use and maintenance, which is another adder.

    Additionally, AI systems involve ongoing operational costs such as model retraining, software updates, license renewals, and regular infrastructure maintenance. These recurring expenses typically amount to 10–20% of the initial investment annually, highlighting the sustained financial commitment necessary for successful AI implementation.


    Takeaway

    Deploying AI in semiconductor manufacturing demands considerable upfront and ongoing investments in infrastructure, software, data management, and skilled talent. However, as semiconductor manufacturing complexity increases at advanced technology nodes, AI integration is shifting from beneficial to strategically essential.

    AI-driven solutions consistently deliver improved efficiency, reduced downtime, higher yields, and significant financial gains. To fully capture these benefits, companies must strategically plan their AI deployments, scale thoughtfully, and maintain realistic expectations to achieve sustained profitability and competitive advantage.