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The power problem

Why energy management is the next frontier for fab competitiveness

by Ravi Jaikumar, Global Product Manager, Real Time and Advanced Scheduling

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What’s Inside

Semiconductor manufacturing has always been energy intensive. Advanced fabs consume 100–200 megawatts continuously, making energy one of the largest operational inputs. But what was once a manageable cost is now a strategic constraint.

Energy prices are rising, regulatory requirements are tightening, and customers are demanding carbon transparency across the supply chain (see Figure 1). Meanwhile, the push to advanced nodes and higher volumes is increasing energy demand faster than efficiency gains can offset. Energy has moved from an operational cost to a core competitive factor. Fabs that address this challenge can strengthen margins, reduce regulatory risk, and improve customer alignment. Those that do not will face increasing exposure to cost pressure, compliance disruption, and customer attrition.

Figure 1: The converging pressures on fab energy management: rising consumption, increasing costs, tightening regulations, and customer demands for supply chain transparency.
Figure 1: The converging pressures on fab energy management: rising consumption, increasing costs, tightening regulations, and customer demands for supply chain transparency.

The scale of the challenge

The magnitude of semiconductor energy consumption is significant and growing rapidly, as shown in Figure 2. Leading manufacturers consume tens of terawatt hours annually, and advanced nodes continue to drive higher energy intensity per wafer. Technologies such as EUV lithography add substantial, continuous power requirements at the tool level.

Across the industry, energy demand is expected to increase through the remainder of the decade, driven by capacity expansion and accelerating demand for AI and high-performance computing. This growth has direct financial implications because energy now represents a meaningful and rising portion of fab operating expenses. At the same time, power availability itself is becoming a constraint. Key regions such as Taiwan, Arizona, and Texas are experiencing grid pressure as fabs compete with data centers, electric vehicle production, and residential demand. Renewable energy adoption is critical, but it is not sufficient on its own to address these structural limitations.

Figure 2: Illustrative growth trend for semiconductor industry energy consumption, driven by advanced node expansion and increasing chip demand. Actual figures vary by region and source.
Figure 2: Illustrative growth trend for semiconductor industry energy consumption, driven by advanced node expansion and increasing chip demand. Actual figures vary by region and source.

Regulatory reckoning

Regulatory requirements are also quickly evolving. Manufacturers increasingly must disclose energy and emissions data, report Scope 1, 2, and increasingly Scope 3 emissions, and meet efficiency requirements tied to incentives, financing, and reporting frameworks (see Figure 3).

The trajectory is clear: transparency today, efficiency mandates in the near term, and broader carbon pricing over time. Having energy management capabilities in place will help fabs avoid future compliance costs and operational disruption.

Figure 3: Global regulatory requirements for fab energy and emissions—EU CSRD, US SEC climate rules, and emerging frameworks in Asia create a compliance imperative.
Figure 3: Global regulatory requirements for fab energy and emissions—EU CSRD, US SEC climate rules, and emerging frameworks in Asia create a compliance imperative.

Customer pressure: Scope 3 comes home

Regulation establishes the baseline, but customer expectations raise the bar. The ability to demonstrate low-carbon manufacturing is becoming a prerequisite for winning and retaining business. Major technology companies have committed to aggressive net-zero timelines across their supply chains, and semiconductor manufacturers are increasingly required to provide energy and carbon data at the product and lot level.

The three pillars of fab energy management

Addressing energy consumption in semiconductor manufacturing requires a multi-layered approach built on three foundational pillars: equipment efficiency, operational optimization, and systemic intelligence (see Figure 4).

Equipment efficiency focuses on hardware-level improvements such as better components, integrated peripheral devices, and heat recovery systems. Operational optimization extracts more performance from existing assets through intelligent scheduling and predictive maintenance, and systemic intelligence integrates production systems, sub-fab infrastructure, and real-time energy data into a coordinated framework for real-time optimization.

While all three pillars are important, long-term advantages will come from systemic intelligence. Energy must be treated as a managed resource across the entire manufacturing system—not just at the tool level.

Figure 4: The three pillars of fab energy management—equipment efficiency, operational optimization, and systemic intelligence. Each builds on the previous.
Figure 4: The three pillars of fab energy management—equipment efficiency, operational optimization, and systemic intelligence. Each builds on the previous.

Use case: smart sleep for equipment and sub-fab systems

The simplest energy-saving intervention is turning things off when they are not needed. But in a fab, “off” is complicated. Equipment cannot instantly switch from idle to production-ready and wake-up times range from seconds to more than 30 minutes, depending on the system. Additionally, sub-fab equipment—pumps, abatement systems, cooling—must coordinate with process tools. Turning something off at the wrong time risks yield excursions or safety events.

Smart sleep systems address this challenge by predicting idle time and transitioning equipment into lower-energy states when conditions allow. By integrating with MES data, they can anticipate production needs and ensure tools return to readiness in time to meet demand. Sleep modes can include pump speed reduction, N2 flow reduction, heater temperature setback, and other energy-saving configurations.

The energy savings are meaningful. Sub-fab equipment alone can represent 30–40% of fab energy consumption. Transitioning from always-on operation to intelligent sleep modes can reduce sub-fab energy by 15–25% without impacting production availability (see Figure 5).

Figure 5: Smart Sleep integrates MES data with equipment control to predict idle duration and coordinate transitions between idle, sleep, and production-ready states.
Figure 5: Smart Sleep integrates MES data with equipment control to predict idle duration and coordinate transitions between idle, sleep, and production-ready states.

Use case: energy-aware scheduling

Traditional scheduling systems optimize for productivity metrics such as cycle time and throughput. Energy consumption is typically treated as a fixed cost rather than a variable that can be optimized.

Energy-aware scheduling introduces energy as a secondary objective without compromising primary goals. By incorporating consumption profiles, equipment efficiency, and time-of-use pricing, scheduling decisions can reduce energy costs while maintaining performance targets. Implementation requires granular energy modeling, rate-structure integration, and multi-objective optimization that balances productivity and energy objectives.

Even modest adjustments such as shifting high-energy processes to off-peak periods or selecting more efficient equipment can yield meaningful savings. Early implementations show potential for a 10–20% reduction in energy costs without measurable productivity impact, as shown in Figure 6. As a software solution, energy-aware scheduling does not require equipment modifications or capital investment; it extracts value from existing infrastructure through smarter decisions.

Figure 6: Energy-aware scheduling shifts high-consumption recipes to low-cost periods while maintaining productivity KPIs. Potential savings: 10-20% of energy costs.
Figure 6: Energy-aware scheduling shifts high-consumption recipes to low-cost periods while maintaining productivity KPIs. Potential savings: 10-20% of energy costs.

Use case: simulation for energy analysis

You cannot optimize what you cannot measure. In many fabs, energy data is still aggregated at a high level, limiting the ability to understand and optimize consumption at a granular level.

As depicted in Figure 7, below, energy simulation extends existing fab models to include detailed energy profiles across process equipment, sub-fab systems, and facility infrastructure. Energy outputs can be viewed alongside productivity metrics and carbon footprint estimates based on grid emission factors.

The real value lies in predictive analysis. Simulation enables teams to evaluate the energy impact of changes in product mix, capacity, scheduling strategies, or equipment configuration before implementing them. It also supports compliance reporting by providing the data needed for emissions tracking and customer disclosures. This capability transforms energy from an unpredictable externality into a manageable variable for Operations, Finance, and Sustainability teams.

Figure 7: Energy simulation provides visibility into consumption by area, equipment, product, and lot—enabling what-if analysis and compliance reporting.
Figure 7: Energy simulation provides visibility into consumption by area, equipment, product, and lot—enabling what-if analysis and compliance reporting.

The integration imperative

While each approach delivers value independently, the greatest impact comes from integration like that seen in Figure 8. Coordinating scheduling with smart sleep strategies can create longer idle windows and unlock deeper savings. Integrating energy data with maintenance systems can identify inefficiencies before failure, while real-time pricing data can inform dispatching without disrupting production.

These cross-domain optimizations require connected systems and shared data models. As complexity increases, AI-enabled decision-making becomes critical. Intelligent systems can continuously evaluate trade-offs between productivity and energy objectives in ways that isolated tools or manual processes cannot.

Figure 8: Integrated energy management connects scheduling, dispatch, equipment control, and sub-fab systems into a coordinated system that optimizes across domains.
Figure 8: Integrated energy management connects scheduling, dispatch, equipment control, and sub-fab systems into a coordinated system that optimizes across domains.

The investment case

Energy management investments compete with other capital priorities, so the case must be made in financial terms. The business value extends beyond direct savings: lower operating costs, avoided demand charges, stronger regulatory readiness, better customer alignment, and reduced exposure to energy-price volatility and grid constraints.

As supply-chain decarbonization accelerates, customer retention and acquisition may become the strategic prize. Fabs that can demonstrate low-carbon manufacturing will be better positioned to win preferred supplier status, and that value can exceed direct energy savings. The investment case varies by fab, region, and market position, but the trajectory is consistent: energy management capabilities are becoming more valuable over time, not less.

What are leading fabs doing?

Leading semiconductor manufacturers are already prioritizing energy management as a core capability (see Figure 9). Investments are focused on granular monitoring, integration of energy data into production systems, optimization pilots, and renewable energy sourcing. Public net-zero commitments show that energy management is becoming part of the operating model for advanced fabs, not only a sustainability initiative. Early adopters are building capabilities that compound over time.
Figure 9: Major semiconductor manufacturers have committed to aggressive net-zero timelines—TSMC and Samsung by 2050, Intel by 2040—driving industry-wide transformation.
Figure 9: Major semiconductor manufacturers have committed to aggressive net-zero timelines—TSMC and Samsung by 2050, Intel by 2040—driving industry-wide transformation.

The path forward

Energy management maturity typically evolves through four stages: visibility, efficiency, optimization, and intelligence. Most fabs today are still building foundational visibility and implementing targeted efficiency improvements. Forward-looking organizations are moving toward integrated optimization and AI-driven systems.

This progression is not optional. Market dynamics, regulatory pressure, and customer expectations will continue to push the industry forward. The only question is how quickly each organization adapts.

Energy as competitive advantage

The semiconductor industry’s relationship with energy is changing. What was once a background cost is now a strategic variable.

Fabs that invest in energy management will benefit from lower costs, stronger customer alignment, improved compliance, and greater operational resilience. Those that do not will face increasing constraints and competitive disadvantage. Energy management is not a distraction from fab competitiveness; it is fab competitiveness.

Applied Materials APG is developing comprehensive energy management solutions for semiconductor manufacturing—from smart sleep systems to energy-aware scheduling, to simulation-based analysis. Learn how we’re helping fabs reduce energy consumption while maintaining productivity.

FAQs

How can a semiconductor fab cut energy costs without buying new equipment?

Many fabs can reduce energy costs by optimizing how existing equipment is used rather than immediately investing in new hardware. Approaches such as intelligent scheduling, automated idle-state management, and energy-aware production planning help reduce unnecessary power consumption while maintaining throughput, cycle time, and production commitments. In many cases, the fastest savings come from making better operational decisions with the assets already in place.
Many electronics manufacturers have public sustainability goals that extend beyond their own operations and into their supply chains. As a result, semiconductor suppliers are increasingly being asked to provide evidence of how products are manufactured, including energy usage and carbon impact. Fabs that can provide accurate, traceable data are often better positioned to meet customer requirements, support procurement decisions, and strengthen long-term supplier relationships.
For many manufacturers, the biggest challenge is visibility. Energy consumption often exists in separate systems and is difficult to connect to specific products, tools, processes, or production decisions. Without detailed insight into where and when energy is being consumed, it becomes difficult to identify savings opportunities, evaluate tradeoffs, or measure the impact of improvement initiatives. Organizations typically achieve better results when energy information is integrated with production and operational data.
Not when they are designed correctly. Modern energy-management strategies focus on balancing manufacturing performance and energy objectives rather than sacrificing one for the other. By using production-aware controls, predictive analytics, and integrated decision-making, fabs can reduce unnecessary energy consumption while continuing to meet production targets, maintain equipment readiness, and protect yield. The goal is optimization, not restriction.
A practical starting point is establishing a baseline of energy consumption and understanding where the largest opportunities exist. From there, fabs can prioritize improvements that deliver measurable business value, such as increased visibility, operational optimization, and cross-functional coordination between manufacturing, facilities, and sustainability teams. Organizations that treat energy as a business metric—rather than only a utility expense—are better positioned to improve resilience, manage risk, and support future growth.

About the Author

Picture of Ravi Jaikumar, Global Product Manager, Real Time and Advanced Scheduling
Ravi Jaikumar, Global Product Manager, Real Time and Advanced Scheduling
Ravi is a Global Products Manager for Real Time Dispatching and Scheduling software solutions for semiconductor front end fabs and Assembly, Test and Packaging factories. Prior to joining Applied Materials Automation Products Group almost two years ago, he was a senior industrial engineer with Qorvo, Inc. He also served as an industrial engineer for ON Semiconductor and was a supply chain consultant with Hyster-Yale Group. He earned a bachelor’s degree in mechanical engineering from Anna University Chennai, and a master’s degree in industrial engineering from the North Carolina State University.
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