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The rapid growth of AI has created a surge in the global energy consumption at a rate never seen before. Today, data centers account for approximately 415 terawatt-hours (TWh) of electricity globally. To put this into perspective, the annual energy consumption of the United Kingdom in 2023 measured at 309 TWh. The International Energy Agency (IEA) projects data centers’ energy consumption will more than double to nearly 945 TWh by 2030 [1]. A single generative AI query can consume up to ten times the power of a traditional search [1]. Meanwhile, data center energy usage in the U.S. is projected to leap from 4.4% to as much as 12% of the national grid by 2028 [2]. This creates a stark reality for the semiconductor industry. Traditional monolithic scaling has hit its physical and economic limits, leaving advanced packaging and heterogeneous integration to define the industry’s trajectory [3].To meet these escalating compute demands, the industry is rapidly shifting toward multi-die architectures, chiplets, and 3D stacking to decrease the amount of energy needed for advanced computing. This transition is fueling explosive growth in the advanced packaging market, which the Yole Group projects will reach $79.4 billion by 2030 [4]. However, stacking chiplets to bypass Moore’s Law exposes massive systemic bottlenecks. Engineers are now fighting interconnect parasitics, navigating complex power delivery architectures, and battling extreme thermal density.In a 3D-stacked architecture, pulling heat away from vertically integrated dies is one of the most pressing engineering challenges of our time. As compute density rises, issues like die warpage and localized thermal hotspots threaten both reliability and yield. The shift toward sustainable AI systems for energy-efficient computing requires breakthroughs in everything from hybrid bonding process flows to advanced thermal interface material (TIM) strategies and liquid cooling integration [6].These are not challenges that any single company can solve in isolation. Whether you are a foundry, OSAT, material supplier, or equipment provider, overcoming these bottlenecks requires pre-competitive, industry-wide collaboration. Foundational capabilities must be built collectively before competitive differentiation occurs.This is the core mission of the SEMI Advanced Packaging and Heterogeneous Integration (APHI) Technology Coalition. By collaborating on common standards, shared research frameworks, cross-vendor interoperability models, and collective technology roadmap congruency, APHI is actively dismantling the barriers to next-generation computing.The APHI community is already tackling these issues head-on. Monthly chapter meetings identify and address these and other issues facing heterogeneous integration. The most recent chapter meetings showcased in depth review of these challenges. Jonathan Abdilla from BESI detailed the technical challenges and collaborative research required for global hybrid bonding process flows. Similarly, Dr. Jie Geng from Indium Corporation led a deep dive into crucial TIM strategies for AI and HPC, exploring hybrid stacking evaluation methods and liquid cooling options to combat GPU die warpage.The future of advanced manufacturing will be defined by how effectively we manage power and heat in heterogeneous systems. We invite you to join this critical conversation at the upcoming SEMIEXPO Heartland (April 29-30 in Detroit, MI) Day 2 will feature dedicated sessions on Thermal Management Power Delivery in Advanced Packaging: From TIMs to Warpage Control, as well as strategies for securing the advanced packaging supply chain.To help shape the standards and shared roadmaps that will power the AI revolution, explore our initiatives and get involved with SEMI Advanced Packaging and Heterogeneous Integration (APHI) Technology Coalition.Rafael Tudela is Senior Technical Marketing Manager at SEMI References[1] International Energy Agency (IEA). (2024). Energy and AI Report. [2] U.S. Department of Energy (DOE) Lawrence Berkeley National Laboratory (LBNL). (2024). Report on U.S. Data Center Electricity Demand and Grid Impact.[3] Semiconductor Packaging News. Advanced Packaging and Heterogeneous Integration. Retrieved from: https://www.semiconductorpackagingnews.com/articles/92402.html [4] Yole Group. (2025). Status of the Advanced Packaging Industry 2025.

The semiconductor industry is hitting a structural inflection point: explosive AI‑driven demand, rapidly rising manufacturing complexity, and stringent sustainability expectations are converging at once. In this context, edge AI deployed directly on tools, sensors, and local controllers, is shifting from experimental to essential, particularly in fabs where milliseconds matter. SEMI’s timely workshop, Smarter Sensors, Smarter Fabs: AI at the Edge in Semiconductor Manufacturing, taking place March 18–19, 2026 in Milpitas, CA, will address this important topic.From sparse sensing to dense instrumentationTwo decades ago, most process tools relied on dozens of sensors per chamber. Today, leading etch, deposition, CMP, and lithography systems routinely integrate hundreds of sensing channels spanning pressure, flow, RF power, optical endpoint, vibration, and chemistry. At 3 nm and 2 nm, process windows are so tight that yield hinges on multivariate understanding of chamber conditions and tool state rather than a few independent alarms. Sensor proliferation has turned fabs into rich data environments—but also exposed the limits of traditional, centrally managed control.Why edge AI is displacing cloud‑only controlConventional architectures push heavy analytics to centralized servers or the cloud, with supervisory systems periodically updating recipes, setpoints, or dispatch rules. Across manufacturing, measured cloud round‑trip times commonly range from 800 to 2,400 ms, whereas edge systems co-located with equipment can respond in 15–45 ms, roughly 50–160× faster. For safety‑ and yield‑critical loops in semiconductor manufacturing, that latency gap is often unacceptable.At the same time, new generations of low‑power neural processing units (NPUs) and edge accelerators deliver tens of trillions of operations per second (TOPS) at single‑digit watt budgets, making always‑on inference viable inside tools, cameras, and controllers. The result is a decisive move toward edge‑native architectures: models execute where data is produced, while cloud resources are reserved for retraining and fleet‑wide learning.Edge AI on the line: control, inspection, and maintenanceIn process control, edge AI is enabling a shift from univariate threshold checks to multivariate models that understand the joint dynamics of sensor streams. Platforms today embed deep‑learning and statistical models directly at or near the tool, performing real‑time endpoint prediction and anomaly detection from high‑dimensional time series. Similar approaches are emerging in lithography and CMP, where local inference helps keep focus, overlay, and removal rate within spec before wafers drift out of control.Inspection and logistics are undergoing a similar transformation. Vision systems with embedded NPUs classify defects at line speed, often above 100 parts per minute, eliminating the need to ship large image volumes to a central cluster. Robots and autonomous mobile robots (AMRs) use local intelligence for short‑horizon planning and collision avoidance, while higher‑level systems focus on global scheduling and optimization.Predictive maintenance is one of the most mature applications: vibration, acoustic, temperature, and pressure data are analyzed locally to detect anomaly signatures hours or days before conventional thresholds trip. Reported benefits include reductions in unplanned downtime, longer component life, and lower maintenance costs when these models are integrated into manufacturing execution systems (MES) and maintenance workflows.Digital twins and agentic AI on top of edge dataDigital twins build on this sensing and edge‑analytics foundation. By maintaining virtual, live‑updated models of tools, lines, and entire fabs, they enable scenario testing, debottlenecking, and root‑cause analysis without putting WIP at risk. Vendors and early adopters report that such twins can shorten process‑node ramps and facility bring‑up by enabling thousands of “what‑if” experiments before physical changes are made.​Agentic AI is now emerging as the orchestration layer above these twins. In semiconductor case studies, agents connected to MES, advanced process control (APC), and planning systems have delivered double‑digit improvements in throughput, cycle time, and tool utilization by autonomously adjusting routing, batch sizes, and scheduling in response to live fab conditions. Other agents mine unstructured engineering notes and fault reports to accelerate root‑cause analysis, turning hard‑won lessons into repeatable, codified behavior.Sustainability as a first‑class requirementSustainability pressures are reinforcing this stack. Semiconductor manufacturing is energy‑ and resource‑intensive, and regulators and customers alike are demanding more transparency and improvement. Edge‑connected monitoring of energy, utilities, and emissions has already helped some fabs cut energy‑related costs by around 20 percent through tighter control of HVAC, process gases, and idle modes. Research initiatives such as imec’s Sustainable Semiconductor Technologies and Systems (SSTS) program are using virtual fab methods and detailed life‑cycle assessment to guide process and equipment choices for lower environmental impact.Strategic takeaways and where to learn moreThe trajectory is clear: fabs that combine dense sensing, edge AI, digital twins, and agentic AI are building toward continuously learning, self‑optimizing operations. Architectures will need to be edge‑first rather than cloud‑only. Simply adding sensors without local intelligence will not deliver competitive advantage, and environmental KPIs are likely to be optimized with the same rigor as yield and cycle time.For practitioners who want to translate these trends into roadmaps, the Smarter Sensors, Smarter Fabs: AI at the Edge in Semiconductor Manufacturing” workshop (March 18–19, 2026, Milpitas, CA) spearheaded by the SEMI Manufacturing Coalitions* will bring together experts in sensing, edge architectures, digital twins, and agentic AI to share concrete deployments and architectures tailored to semiconductor fabs.*The SEMI Manufacturing Coalitions include Smart Manufacturing, Fab Owners Alliance (FOA) MEMS and Sensors Industry Group (MSIG), Advanced Packaging Heterogenous Integration (APHI) and Semiconductor Components, Instruments, and Subsystems (SCIS). Anshu Bahadur is Senior Program Manager, Technology Communities at SEMI. Mark da Silva is Senior Director, Manufacturing Coalitions at SEMI.

The semiconductor industry is the bedrock of modern technology, enabling everything from AI and cloud computing to electric vehicles. Yet, this critical sector is also one of the most resource-intensive globally, with a substantial dependency on water. A single fabrication plant can demand up to 10 million gallons of water daily, comparable to the consumption of a city with 300,000 residents. Much of this water is, of course, reused and recycled through sophisticated systems. This immense water usage, particularly the requirement for ultrapure water for processes like cleaning and etching, makes consistent access to high-quality water a non-negotiable for operational reliability and business continuity. The new insights report "Ripple Effects: Water Risk and Resilience Across the Semiconductor Value Chain" provides the first global baseline of water risk hotspots for the semiconductor sector, assessing water risks across 140 facilities across 89 water basins to inform future risk mitigation strategies.The analysis discusses how water risk can manifest itself as a financially material impact on business continuity by triggering idle time, recovery costs, and cascading delivery delays across global supply chains. S P Global projects that by 2050, water-related risks could cost the world's largest IT companies up to $24 billion annually. Crucially, the study identified flooding and reputational risks—such as strained relationships with local communities over water allocation—as the most significant immediate threats to the semiconductor value chain. These concerns are most acute in major hubs like Taiwan, South Korea, and parts of the U.S.While the industry is frequently criticized for its water usage, only 16% of the analyzed sites are currently affected by water scarcity. However, this metric offers a false sense of security. As climate change intensifies, the frequency and severity of water-related disruptions are set to exceed the scope of existing contingency plans. The long-term projections show that over 40% of semiconductor facilities announced since 2021 are located in watersheds projected to face high or extremely high water stress between 2030 and 2040. This underscores the urgent need to integrate forward-looking risk modeling into new site planning to ensure long-term operational resilience.Effective risk management is significantly hindered by the limited transparency surrounding supplier-level water data. While many companies perform water assessments for their direct operations, a comprehensive, industry-wide approach to supplier data and risk management is lacking. CDP data shows that 1 in 5 companies reported $77 billion under threat from supply chain water risks, yet only half of those companies engage with their suppliers on these issues. For semiconductor end users, these risks are often deep within multi-tiered networks, requiring engagement that goes well beyond Tier 1 suppliers.To manage these complex risks, the report stresses the necessity of moving toward a contextual approach that includes localized assessments. Contextual water risks are inherently location-specific, dependent on local availability, quality, and infrastructure, as well as broader catchment-level dynamics, regulatory pressures, and community expectations. Several structured methodologies support this necessary shift from basic operational management to corporate water stewardship, including the Alliance for Water Stewardship (AWS) Standard, the TNFD's LEAP framework, and the Science Based Targets for Nature (SBTN). This approach encourages companies to look beyond their own operations to safeguard regional water security.Because water is a shared resource, collective action is essential to deliver the scale and urgency needed to tackle common challenges within catchments. The semiconductor value chain is deeply interconnected, with companies often sharing suppliers within the same water basins, creating a strategic opportunity for collaborative stewardship. The report encourages companies to scale their impact by moving beyond isolated efforts to form sector-wide and cross-sector partnerships—especially at the catchment level—through public-private engagement. This collaboration, which includes proactive engagement with policymakers and local utilities, is key to aligning on water management and stewardship practices to address shared water challenges and build collective trust.Innovation and technology must play a central role in advancing water stewardship across the value chain. A major hurdle is the general undervaluation and mispricing of water, which perpetuates systemic underinvestment in water-focused technology. Despite this, leading semiconductor companies are deploying advanced solutions such as onsite recycling systems, real-time water monitoring, and utilizing alternative sources like municipal wastewater. Embracing AI-driven systems for scenario modeling and catchment-level risk forecasting further enhances adaptive capacity and resilience.The "Ripple Effects" report makes it clear that water challenges affect every segment, demanding tailored response tactics and strategies. Foundries, with their large operational footprints, must prioritize sourcing reclaimed water and expanding onsite reuse, while chemical and materials suppliers must proactively manage rising regulatory risks around water quality contaminants. The insights report also provides a practical roadmap for advancing corporate water stewardship, outlining progression from water risk assessment (Stage 1) to site-level action and collective engagement (Stage 2), and culminating in transparent validation and reporting (Stage 3). By following a structured water stewardship pathway, the semiconductor industry can build operational resilience and ensure a responsible future for the entire value chain.To learn more, download the report or watch the webinar recording. Alua Suleimenova is Senior Program and Staff Manager | Global Sustainability at Marvell Technology and leader of SEMI's ERMR Working Group.The Environmental Risk Mitigation and Reporting (ERMR) Working Group was established under SEMI's Sustainability Initiatives in January 2023, and it aims to develop a baseline and roadmap of best practices for identifying, managing, governing and reporting climate, water, and biodiversity risks across the semiconductor value chain. This insights report is a publication in SEMI’s ERMR Working Group thought‑leadership series on global environmental risks and resilience.

The New Reality of Semiconductor Supply ChainsThe semiconductor industry is one of the most globally connected and time-sensitive sectors in existence. From design and wafer fabrication to testing, assembly, and delivery, every step depends on flawless coordination across borders, suppliers, and time zones. More than 1 trillion chips move through global supply chains each year[1], connecting suppliers, foundries, and fabs across continents.But as the world becomes more complex and unpredictable – with trade tensions, capacity shortages, shifting technology cycles, and driven by increasing AI and energy demand – that coordination is increasingly under pressure. Even a minor delay in one place can halt production in another. A single missed component, delayed shipment, or grounded flight can trigger line-down costs that can exceed $4 million per hour in advanced fabs[2].That’s why logistics is no longer a background process; it’s a strategic function that directly influences performance, reputation, and profitability.Figure 1 - We live in a complex and unpredictable world where global events have significant impacts. When Every Hour MattersFew industries feel the impact of delays as directly as semiconductors. One missing component, a grounded flight, or a customs delay can trigger a chain reaction that stops production lines, delays product launches or breaks contractual commitments.Time-critical logistics plays a distinct role in safeguarding supply chain performance, staying in control and enabling companies to recover in hours instead of days. It serves as the system’s shock absorbers when conventional routes are disrupted, timelines collapse, or customer commitments are at risk.The ROI of UrgencyTime-critical shipments are costly—but in the semiconductor world, not acting fast is far more expensive and pose significant consequences. A single line-down event can cost millions of euros per day, depending on the process stage and the customer involved. Compared to that, the premium for a same-day or next-flight shipment is minimal.When companies integrate time-critical logistics as a planned capability, the ROI becomes tangible.Avoided downtime: Faster recovery after supply interruptions directly protects production yield and customer commitments.Reduced inventory buffers: If rapid response capacity is available, less working capital is tied up in safety stock.Customer retention: Reliable continuity strengthens trust and long-term business relationships.In this sense, time-critical logistics isn’t just an operational expense – it’s a continuity investment. It protects revenue streams and reputation and gives manufacturers the agility to respond to whatever the next disruption brings.Example scenarios include:A single fab tool delay can idle an entire production line, costing millions per hour. During the 2024 Taiwan earthquake, a single supplier delay triggered hundreds of millions in global production losses.A next-flight-out or onboard courier shipment typically represents less than 1% of that downtime cost.Rapid recovery also prevents ripple effects such as delayed customer deliveries.The companies that embed time-critical logistics as a strategic capability gain not only cost protection, but competitive agility.From Efficiency to AgilityTraditional supply chains were built for stability and scale: move high volumes, keep costs low, and plan far ahead. But in the semiconductor industry, speed and adaptability now define competitiveness.Agility means having the systems, partners, and mindset in place to act decisively when the unexpected happens. Leading companies are now integrating dedicated control towers, predictive data insights, and predefined emergency logistics playbooks – turning reaction time into a measurable performance indicator. However, agility is not only a matter of infrastructure – it depends on data-driven visibility and cross-industry collaboration.Collaboration as the Real DifferentiatorNo company can face disruption alone. Semiconductor supply chains depend on the combined coordination of equipment makers, material suppliers, foundries, logistics providers, and government agencies.Collaboration is therefore the new competitive edge. Shared standards, visibility tools, and coordinated crisis response frameworks – like those developed under the SEMI Supply Chain Management Initiative – are helping the industry build collective resilience. These cross-functional efforts are where innovation scales.Looking AheadThe semiconductor industry will continue to expand into new regions and technologies. Each step adds complexity and, with it, vulnerability. The next decade will test not just how fast companies can produce, but how fast they can recover. Future disruptions – whether geopolitical, environmental, or digital – are inevitable. The question is not how to avoid them, but how to respond faster and smarter when they occur.That’s where time-critical logistics will continue to make its mark. It is more than just a transport solution. It gives companies the ability to act decisively in moments that matter most – transforming time from a constraint into a competitive advantage that ensures business continuity.Key TakeawaysSemiconductor supply chains are uniquely time-sensitive — a single delay can halt multimillion-dollar production lines.Integrating time-critical logistics improves responsiveness, reduces inventory needs, and safeguards business continuity.Agility, not efficiency, will define the next decade of semiconductor competitiveness.Collaborative industry frameworks like the SEMI Supply Chain Management Initiative are key to building resilience.How ready is your supply chain?Learn more about time:matters’ tailored logistics solutions at SEMICON Europa 2025 (Hall C2, Booth 433), November 18-21 in Munich and attend the company’s presentation on the TechARENA stage on November 19. For more information or to schedule a meeting at SEMICON Europa, click here to contact Remy Schoenzetter.Remy Schoenzetter is Global Head of Business Unit High Tech Semicon at time:matters.[1] Statista: Global semiconductor unit shipments 2021; SIA/WSTS Annual Reports[2] McKinsey: "Need to boost semiconductor fab efficiency?" (2023); LinkedIn Air Monitor analysis (2025); Critical Manufacturing Industry Blog (2024)

The semiconductor and electronics industries are at a turning point. Once defined by efficiency and scale, supply chains now face a convergence of pressures—from geopolitical tensions and climate risks to accelerating innovation cycles. The stakes are higher than ever, but so are the opportunities to reimagine how this global ecosystem operates.The End of “Just-in-Time” as We Knew ItIn 2025, one thing is clear: the old “just-in-time, globally concentrated” supply chain model can no longer carry the industry forward. Trade policies are tightening, export controls are multiplying, and tariff investigations are fragmenting markets that once felt seamlessly connected.At the same time, natural resource risks are mounting. PwC estimates that by 2035, nearly one-third of global semiconductor production could face copper supply disruptions caused by climate change. That figure rises to nearly 60% by 2050 if emissions remain unchecked. Add to this the growing maze of regulatory barriers and import restrictions on raw materials, and the industry faces rising procurement challenges and relentless cost volatility.Demand Isn’t WaitingWhile supply chains struggle with constraints, demand continues its upward climb. Global chip sales are rebounding, driven by innovation cycles in AI, automotive electronics, 5G, and renewable energy. Bringing new manufacturing capacity online takes years. The imbalance is widening, and companies can’t afford to rely on outdated, reactive supply chain models.Resiliency has become mission critical. And as the saying goes: you can’t respond to risks you can’t see. Guesswork isn’t a strategy—especially when disruptions are systemic.Fragility in a Fragmented EcosystemSemiconductor production is specialized and geographically fragmented. A disruption at a single node—whether a mine, a fab, or a logistics hub—can ripple through the ecosystem in days or even hours.Recent shocks have only reinforced this fragility:Trade restrictions are pushing manufacturers to rethink supply chain design.Climate change is endangering raw materials like copper and quartz, both highly water- and energy-intensive to produce.Market volatility is being driven by the explosive rise of AI and data center demand.The lesson is simple: resilience is no longer optional—it’s an existential requirement. And the path to resilience runs through visibility, agility, and collective intelligence.Real-Time Intelligence: From Luxury to NecessityIn today’s environment, quarterly or even monthly reporting cycles are dangerously slow. By the time a shortage, tariff, or logistics reroute appears on the radar, the window to act may have already closed. The cost of waiting—or doing nothing—is steep, and the damage can be lasting.Real-time data and AI-driven insights aren’t “nice-to-have” tools anymore. They are strategic imperatives for supply chains under constant stress. They allow companies to anticipate risks, respond faster, and align more effectively with partners across the ecosystem.Collaboration Is the New CurrencyNo company can go it alone. A chipmaker depends on its suppliers, just as a rare earth miner depends on transport partners. The global supply chain is a living system—and its resilience depends on the strength of its interconnections.Deeper supplier relationships, visibility into Tier 2 and Tier 3 suppliers, and shared intelligence on geopolitical and regulatory shifts are all critical. Resiliency isn’t built in silos; it’s forged through collective action.Building the Future TogetherThe semiconductor and electronics industries stand at the threshold of a new era—one of collective risk but also shared potential. Companies that embrace transparency, real-time intelligence, and collaboration will not just survive shocks, but emerge stronger, more agile, and better prepared to lead.In this new chapter, collaboration is the currency of resilience.That’s where Conductor™ comes in: a real-time intelligence platform built to help industry players anticipate, adapt, and act – together. Conductor weaves all those threads together, delivering not just data, but a shared situational awareness, helping the industry to think and act as a system rather than a collection of silos.What Conductor Enables - and What It Could Lead ToSmarter, faster decisionsA platform like Conductor, which uses near real time data, AI-powered news and alerts, and community-driven insights, turns reactive “damage control” into proactive “risk management.”By bringing together cross-segment, critical KPIs, curated AI news, expert analysis, and peer-community intelligence, Conductor helps teams understand what’s happening now, assess the likely impact on their business, and decide how to respond - faster, and with more context.Over time, this could shift the default mode of the industry from “fire-fighting” to “anticipatory steering.”A more adaptive supply chainAs more organizations adopt the platform, the collective visibility improves. Conductor can power scenario planning, enable early warning systems, and foster agile “micro-pivot” strategies: reroute logistics, adapt sourcing, or reallocate production before a disruption becomes a crisis.New models of ecosystem resilienceWith consistent, shared intelligence, industry players can identify common vulnerabilities and coordinate mitigation for mutual gain. Over time, this could lead to more resilient operations through diversified sourcing strategies, and even shared contingency mechanisms.In short: Conductor is a building block toward a more distributed, more transparent, more resilient global semiconductor ecosystem.Accelerated innovation cyclesWhen the risk of disruption is better managed, companies can operate with more confidence, investing in new capacity, experimenting with new chip architectures, or integrating new markets more aggressively. Technology diffusion accelerates when the fear of “what-if” is reduced.Where We Go From HereConductor is already in early-access pilot phase, and feedback from the SEMI Supply Chain Management Initiative’s Industry Advisory Council is actively shaping its evolution.As adoption spreads, network effects will increase the platform’s predictive power, making it more valuable for everyone involved.In an industry that’s increasingly defined by fast change and high stakes, tools like Conductor shift the balance: from reactive scramble to informed strategy, opaque fragility to visible resilience, and from isolated action to ecosystem collaboration.The future of supply chain resilience starts here. Sign up for early access to Conductor today and help drive the new era of trade.Talal Abu-Issa is Co-CEO and Co-Founder of Beebolt.Krish Dharma is Strategic Advisor, SEMI Supply Chain Initiative.

Use of machine learning and artificial intelligence (ML/AI) is on an exponential rise across fields1 including all aspects of the semiconductor industry. In the last decade, the use of ML/AI exploded in the areas of speech recognition, facial recognition, smart phone features, search engines and now large language models like ChatGPT, Bard AI, and CoPilot. The ML/AI growth has been enabled by massive data storage capacity and increased compute performance, leading to projections for the semiconductor industry to reach over $1 trillion in annual revenue by 2030, with about 50% of the industry’s growth related to GenAI2. Figure 1: McKinsey Company on GenAI driving semiconductor industry growthAs semiconductor manufacturing drives toward Industry 4.0, SEMI member companies have a vision of Industry 5.0, truly adaptive manufacturing, integrating human creativity with robotic precision enabled by AI. Along that path, automation and data exchange in every step of manufacturing is essential, with data acquisition, data integrity and relevance, and operational Digital Twins3 as defined steppingstones to the factory of the future.Based on growing member interest in ML/AI, in 2019, SEMI assembled technology communities that quickly engaged in AI discussions and proofs of concept, discovering gaps in the path to Industry 4.0. Successful demonstrations of the value of AI in chip manufacturing process development and factory efficiency, not to mention GenAI uses in society, hastened the pace to produce faster, more powerful chips to accommodate the computation and communication requirements. Recognizing the industry opportunity and the mounting role AI plays in the semiconductor supply chain, SEMI initiated several thought leadership efforts, namely the Smart Manufacturing Initiative, Smart Data-AI Initiative, and the Future of Computing think tank.Smart Manufacturing According to the SEMI World Fab Forecast, over 100 new and expanded wafer fabs will begin volume production by 2027. This massive capacity expansion will need to achieve the highest possible operational efficiency and performance. To this end, the Smart Manufacturing Initiative is a technology community with over 120 member companies collaborating pre-competitively to transform manufacturing. The SEMI Smart Manufacturing Global Executive Committee (GEC), outlined a roadmap vision for the cognitive factory of the future based-on technology, sustainability and future talent. The GEC has been working with members to realize that vision. Figure 2 describes this vision in terms of the technology progression needed and the approximate timeline for implementation by most manufacturers. The proliferation of this vision through Smart Manufacturing Forums at SEMICON events around the globe, newsletters and blogs has garnered enormous interest and participation in the initiative and is central to the mission of connecting and raising awareness within the ecosystem. Figure 2: AI-Driven Smart Factory (Point Systems to Autonomous Solutions) To move the needle on this vision, industry experts in the initiative successfully created and launched the Industry 4.0 Readiness Assessment Model (IRAM) to help assess technology deployment progress. IRAM adoption is steadily growing. Modern front-end and back-end lines produce an extraordinary amount of multi-modal data from a variety of sources, and this is key to success in unlocking the potential of AI in manufacturing environments. The initiative’s global working groups on Data Architectures and Smart Control Room among others are working towards a holistic Cognitive Factory framework uniting the vertical and horizontal flow of information. Integral to the Cognitive Factory are smart manufacturing standards, that will accelerate the vision outlined above, and without which local solutions are unlikely to scale.In 2023, the Smart Manufacturing Initiative brought together industry leaders in a unique Digital Twin workshop to align on the state of semiconductor development and usage. The key takeaways from this workshop are captured in a white paper that highlighted the need to accelerate efforts in multiple areas including standards. Along with SEMI International Standards, Smart Manufacturing supports other standards development organizations (SDOs) and NIST standards development, for example, to identify and drive critical standards for Cognitive Factory implementation. The initiative is planning future workshops on Cognitive Factory Framework requirements, Digital Twins, and Smart Data AI in the coming months. that highlighted the need to accelerate efforts in multiple areas including standards. Along with SEMI International Standards, Smart Manufacturing supports other standards development organizations (SDOs) and NIST standards development, for example, to identify and drive critical standards for Cognitive Factory implementation. The initiative is planning future workshops on Cognitive Factory Framework requirements, Digital Twins, and Smart Data AI in the coming months.The GEC has identified critical interrelationships in addition to the technology focus. At the intersection with sustainability, the initiative has formed a collaborative task force with the SEMI Semiconductor Climate Consortium (SCC) to develop a bottom-up technology roadmap that can be used as a blueprint for device makers to meet their proclaimed sustainability goals faster. The task force organized a technical session at SEMICON West 2024 and will be releasing a white paper in the near future. Similarly, the initiative is working with the SEMI Foundation to identify necessary future skills and to make training available through SEMI University. Smart Data AI – Applying AI to Semiconductor OperationsSEMI’s Smart Data-AI Initiative started by assembling a group of interested companies to explore the pivotal role AI could play in the industry and to address the criticality of data. All stakeholders agreed that a formidable challenge was (and still is) the integrity of that data and the security of sharing that data, which is considered IP to most. The optimal implementation of ML/AI techniques can only be gained by access to the comprehensive data set which is owned by numerous supply chain partners. Consequently, semiconductor R D, process and design have not yet realized the full benefit of Data-AI advances. In response, the initiative developed a framework to create value for members and support industry progress. Four pillars underpinning the strategy are:Educating stakeholdersBuilding communitiesExecuting proof-of-concept projectsDeveloping industry standardsTo explore the data challenges the subject matter experts highlighted, a collaborative proof-of-concept (POC) project was proposed in 2019 and accepted by the initiative's partners at Army Research Laboratories4 along with academic and industry partners. The project has completed two phases and is starting on its third phase under the expert guidance of an Industry Advisory Council (IAC) comprised of leaders in the Smart Data-AI community.The POC project, being conducted by principal investigators at Cornell University, demonstrated significant accomplishments from the first two phases, including:An AI model to predict device geometry by optimizing photolithography and plasma etching processesInitial demonstration of secure data-sharing techniques with software-hardware co-optimizationInnovative metrology ideas to train AI algorithms rapidlyStudents trained in cross-disciplinary skills to address the industry’s critical talent shortageFurthermore, the visionary objectives laid out at the initial stages of the POC proved to be synergistic with the strategic goals of the CHIPS Act5, which articulates the need for “collecting, aggregating, and sharing data sets that enable benchmarking and operational improvements, tools development, the creation of digital twins, and training AI models,” and that “the NSTC could develop a methodology for the voluntary sharing of data that protects the proprietary component and national security while enabling access to appropriate performance data.” Phase 3, to be completed by August 2025, will advance the state-of-the-art toward the following specific objectives:A framework to create and integrate Digital Twins of semiconductor R D and manufacturing process toolsAbility to explore processes and generate virtual devices swiftlyDefined interfaces to combine models for each process module or toolAccurate AI-based models for executing virtual process flows to build virtual devicesAdvanced solutions for secure data-sharing across the ecosystem – for example, federated learning where raw data is protected for each entity by building models locally, and only the outputs of the local models are used to build flow-level AI modelsFoundation for future industry standards for secure data-sharing and for interfaces in the virtual innovation environmentSEMI continues to build the collaborative community for Data-AI and strives to synergize with broader efforts such as the Digital Twin Manufacturing Institute, NSTC, and NAPMP in the U.S., and international standards development. Smart Data AI – System-level Innovation for AI – Future of ComputingThe cross-collaborative and synergistic objectives of Smart Manufacturing, the Smart Data-AI proof-of-concept work, and SEMI Standards merge to advance the state-of-the-art. The objective is to help members realize the full value of technology and innovation. In addition to improving semiconductor operations using AI, the efforts also strive to enable SEMI members to participate in, and ultimately profit from, market growth opportunities. Continued progress in AI is crucial both for the industry’s march towards $1 trillion in annual revenue, and for continuing to realize AI’s benefits to society.There are some hurdles to overcome in such a dynamic market. AI models, and the data they process, are outpacing hardware advances, posing a major roadblock for continued progress. As GenAI becomes more pervasive, the performance and power challenges continue to multiply, and require significant innovation in both hardware and software. While individual companies will develop competitive products in this domain, the entire ecosystem needs to evolve in a synergistic manner. As a global industry association, SEMI can play an important role in ensuring this. SEMI started a series of workshops and technology sessions to develop the community and identify opportunities and challenges. The first in this series was a joint workshop with McKinsey Co., held in October 2023, with a focus on innovations in “Domain-Specific Architectures.” Strategically, it brought together thought leaders from three diverse communities - start-ups, investors, and SEMI member companies across the supply chain. This was followed by an overcapacity audience at the Future of Computing session at SEMICON West 2024, where we explored AI-specific hardware with leaders in academia and industry. The Initiative’s next planned event in October 2024 is a focused workshop that is designed to be highly interactive and bring together visionaries and thought leaders from across the value chain – materials, devices, architectures, algorithms, and critical enabling technologies such as photonics, chiplets, advanced packaging, and 3D and heterogeneous integration. The overarching goal is to identify pre-competitive collaborative actions that would help the entire industry. The “Future of Computing” is the broad path to the industry’s future success. While AI systems are the current major wave on this path, future waves may be about heterogeneous integration of photonics and other components, and ultimately, quantum technologies joining the mainstream. SEMI continues to monitor these future trends, strengthen the ecosystem and enable innovation through pre-competitive collaboration, and accelerate implementation through standards.SEMI is fostering today’s collaborations while helping the industry navigate the future of electronics.Melissa Grupen-Shemansky is CTO at SEMI, Pushkar Apte is a Strategic Technology Advisor and Leader of the SEMI Smart Data-AI Initiative, and Mark da Silva is Senior Director of the SEMI Smart Manufacturing Initiative.Definitions and References:1https://arxiv.org/abs/2405.15828 Eamon Duede, William Dolan, Andre Bauer, Ian Foster, Karim Lakhani2McKinsey Company3Digital Twins for semiconductor manufacturing operations are dynamic, predictive, data-driven virtual models of a physical asset, process, or an entire factory, constantly synchronized with its real-world counterpart through real-time data streams and analytics4Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-19-2-0345. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.5“A Vision and Strategy for The National Semiconductor Technology Center (NSTC)” published by the CHIPS R D Office.

In recent years, SEMI has been increasing its attention to sustainability within the global semiconductor industry. With the formation of the Semiconductor Climate Consortium (SCC) within the SEMI Sustainability Initiative, member companies have joined hands to collaborate and collectively tackle the challenge of meeting the industry’s ambitious sustainability goals.The SEMI Smart Manufacturing Initiative is a global effort focused on the future of manufacturing in electronics that helps microelectronics manufacturers enable business value by facilitating awareness and problem-solving to reduce barriers and realize timely ROI for critical Industry 4.0/5.0 technology deployment.Addressing the Industry’s Sustainability Inflection Point The Initiative’s Accelerating Sustainability with Smart Manufacturing Task Force formed in the summer of 2023 is a pivotal effort that collaborates with the SEMI Sustainability Initiative. The task force provides the “how” to the “what” of corporate sustainability goals, focusing on a bottom-up approach that leverages various sensing technologies, at the cleanroom, sub-fab and facilities levels for both greenfield and brownfield device-making facilities, to enable predictive analytics.The task force is designed to be a driving force to encourage the industry’s shift from a fragmented, top-down approach – which does not address the increasing usage of carbon as device-making becomes more complex – to a more integrated, bottom-up strategy. The task force has created a roadmap based on three pillars comprising connecting, sensing, and predicting technologies, which are meant to be cumulative and could dramatically reduce the carbon footprint for making microelectronics like semiconductors. The task force participants believe this to be the first industry roadmap to address all areas of fab processing, leveraging Industry 4.0/5.0, including AI for the purpose of enhancing the sustainability of operations.The task force’s roadmap is comprehensive and includes a variety of sensing technologies, digital twin methodologies, and machine learning/AI techniques for reducing Scope 1 (direct, process-based) and Scope 2 (indirect, energy-related) greenhouse gas (GHG) emissions, water usage and material waste. Once the roadmap is completed, factory management can prioritize the implementation of best practices based on the quantified impact factors of specified use cases. The roadmap outlines methodologies for brownfield and greenfield fabs separately based on practical capex and implementation recommendations.The task force has finalized its assessment of practical solutions for reducing Scope 1 and Scope 2 emissions, and it is now addressing water usage best practices before addressing material waste later this year and into 2025. SEMI will publish separate white papers regarding Scope 1 and 2 as a precursor to offering the roadmap as a customized tool for fabs.The semiconductor manufacturing industry stands at an inflection point from a sustainability perspective. Consider this: U.S. CHIPS Act funding is set to support 19 greenfield fabs, which collectively will consume the equivalent of 11 Empire State Buildings’ worth of steel. That’s a monumental environmental impact. Additionally, the Q2 2024 SEMI World Fab Forecast is tracking approximately 104 new fabs worldwide coming online between 2023 and 2027. Each of these fabs requires significant amounts of concrete, steel, and construction equipment, all with their own GHG footprints. This all represents a challenge that the industry must grapple with head-on.Furthermore, there is the energy-intensive nature of large semiconductor fabs, the cost implications of renewable power acquisition, and the substantial water usage by medium and mega-sized fabs. The main mission of the task force is to create an industry roadmap—a practical blueprint—for device makers to invest in sustainability, following a systematic, bottom-up approach.Accelerating Sustainability with Smart Manufacturing at SEMICON West At the Smart Manufacturing Pavilion at SEMICON WEST 2024 , a special session centered around the key components of the Accelerating Sustainability with Smart Manufacturing Task Force roadmap. Task force leaders helped coordinate a unique session to showcase the roadmap findings and detailed case studies for Scope 1 and 2 emissions. The session included speakers from Deloitte, Linkan Engineering, Micron, Solvay, Spectrum Environmental Services, and ULVAC.Accelerating Sustainability with Smart Manufacturing Session at SEMICON West 2024The opening keynote by Adeline Tay of Micron covered the company’s pioneering efforts to improve sustainability in semiconductor manufacturing. Tay highlighted how to enable a net-zero transition sooner via smart manufacturing technologies including digital twins, real-time power monitoring and optimization, lower global warming potential gas usage, and emission data visibility.The next presentation by Brian Coppa (Task Force Co-Chair) of ULVAC [1] revealed the findings of the SEMI task force roadmap, which described the most critical technologies to reduce Scope 1 and 2 emissions and quantified them with respect to impact level to help fab managers prioritize budget allocation for meeting related sustainability goals at the cleanroom, sub-fab and facilities levels. Coppa provided estimates on the substantial decreases in emissions that can be achieved (see Figure 1) using the compilation of recommended technologies (i.e. predictive analytics such as AI, machine learning and predictive maintenance) that are outlined in the task force roadmap. Figure 1: Task force roadmap estimates for the emissions reduction potential for Scope 1 and 2, respectively.Jake Townsend of Deloitte [2] followed and underlined the industry’s sustainability challenge with regards to chip-making carbon footprint and the opportunities from design to fab, to sub-fab and facilities. Townsend presented that the industry’s water management carbon footprint is estimated to be at least 2 orders of magnitude higher by weight compared to consumer products such as a car or hamburger.Next, Steve Hall of Spectrum Environmental Solutions [3] discussed how compliance testing and measurements differ from common GHG reporting, which is calculation based. The Electronics Code for Federal Regulations 40CFR 98 Subpart 1 now requires the electronics industry to report both calculated emission factors and smokestack measurements. Hall showed the benefits of Fourier-transform infrared spectroscopy (FTIR) monitoring at the smokestack level and the accuracy it can provide for reporting.Michael Peter Pitroff of Solvay [4] provided an in-depth case study on how the F2-based cleaning gas Solvaclean® can replace SF6 chemistries for chamber cleans, facilitated by in-situ gas monitoring techniques, to reduce Scope 1 emissions in plasma etch and deposition processes. Pitroff covered the financial and throughput impacts of the replacement and described how higher global warming potential (GWP) gases can be replaced with low GWP gases in Bosch wafer etch processes.Finally, Drew Horseman of Linkan Engineering [5] presented innovations in water treatment for the semiconductor industry. Horseman covered current state-of-the-art, energy-efficient reverse osmosis (RO) systems and addressed specific benefits of RO optimization. He showed how data acquisition and analytics are the first step to eventually reaching zero-liquid-discharge (ZLD), which is the future of sustainable water treatment.Get Involved!Overall, the Smart Sustainability session at the Smart Manufacturing Pavilion at SEMICON West 2024 enlightened many attendees on how smart manufacturing technologies like AI can help advance sustainability in semiconductor manufacturing using a more scalable, automated approach. Visit the SEMI Smart Manufacturing Initiative homepage to learn more about upcoming activities and contact [email protected] to get involved in the task force.Amit Srivastava is Manager, Data Science- Smart Manufacturing and Artificial Intelligence at Micron Technology, Brian Coppa is Product Engineering Lead at ULVAC, and they lead the SEMI Accelerating Sustainability with Smart Manufacturing Task Force. Mark da Silva is Senior Director of the Smart Manufacturing Initiative and APHI Technology Community at SEMI. Topics: Smart Manufacturing , SEMI Smart Manufacturing Initiative , semiconductor manufacturing , Digital Twin standards , manufacturing productivity , Digital Twin framework , manufacturing efficiency , semiconductor industry , semiconductor ecosystem, sustainability , SEMICON West , decarbonization , Semiconductor Climate Consortium , greenhouse gas emissionsReferences from the Smart Sustainability Session at Smart Manufacturing Pavilion available to SEMICON West 2024 attendees: SEMI Smart Sustainability Roadmap: Blueprint for Device MakersGreen Chips are the New Blue-Chip InvestmentMeeting Net Zero Manufacturing Challenges with Real-Time Monitoring of Exhaust Laterals in Sub-FabsSolvaClean as replacement of SF6 in cleaning and etchingInnovations in Water Treatment: Exhibiting the Importance of Data in Sustainable Process Development

Leveraging predictive modeling and other efficiency-enhancing innovations, Digital Twins promise to optimize semiconductor design, manufacturing processes and equipment maintenance while improving overall operational efficiency.