Implementing advanced power efficiency design is now critical in our computing-driven world. Every electronic system, from tiny sensors to vast data centers, must maximize performance yet minimize energy use. This essential balance, therefore, drives relentless innovation across hardware, software, and materials science. Furthermore, global industries grapple with high operational costs and the urgent need for sustainable practices. This article thus explores cutting-edge strategies and technologies. These innovations define the next generation of next generation of energy-efficient electronics, spanning integrated circuits to AI-driven optimization.
The Silicon Core: Advanced Power Efficiency Design in IC Design Techniques
Integrated circuits (ICs) are fundamental to modern technology. However, as engineers constantly seek smaller and faster devices, power consumption becomes an increasingly critical concern. Consequently, various “low-power design” techniques are employed to address this challenge, forming the core of modern power optimization. These methods, for example, effectively reduce both dynamic and static power loss in complex silicon. Ultimately, such strategies are vital for creating the energy-efficient processors that power everything from smartphones to supercomputers, showcasing true advanced power efficiency design.
Clock Gating: Contemporary Energy Reduction Strategies for Unnecessary Activity
Integrated circuits primarily consume dynamic power, which originates from charging and discharging capacitances during transistor switching. Clock signals, while essential for synchronizing chip operations, also contribute significantly to overall power usage. To mitigate this, clock gating is a common and effective technique that minimizes dynamic power, embodying cutting-edge power management. Specifically, it works by disabling clock signals for inactive circuit parts; therefore, if a block is not actively working, its clock signal pauses. Essentially, this action stops needless switching, thereby saving energy without impacting performance. Indeed, one can consider this similar to turning off lights in an empty room, a simple yet profound energy saving innovation.
Power Gating: A Key Advanced Power Efficiency Design for Idle Blocks
While clock gating handles dynamic power effectively, static power—also known as leakage power—increasingly dominates as transistors continue to shrink. This leakage current, in fact, flows even when a transistor is nominally “off.” Consequently, power gating offers a more comprehensive solution for next-gen efficiency techniques; it fully cuts power to idle circuit blocks. Specifically, it employs “sleep transistors” to physically isolate these blocks, thereby eliminating static power in those specific areas. This robust technique, therefore, works exceptionally well for designs where chip parts remain inactive for extended periods, ultimately saving significant power in devices like smartphones and embedded systems. This illustrates a key principle of advanced power efficiency design.
DVFS: An Advanced Power Efficiency Design Adapting to Workload Demands
Dynamic Voltage and Frequency Scaling (DVFS) is a powerful and, indeed, extremely common technique for achieving advanced power efficiency design. Essentially, DVFS dynamically adjusts the supply voltage and frequency in response to a processor’s real-time workload. The underlying idea is quite simple: lowering voltage profoundly affects power consumption, since dynamic power drops quadratically (Power $propto$ Voltage²). Furthermore, reducing the operating frequency also allows for a lower voltage, thereby maximizing efficiency. This is a core strategy in modern power optimization.
For instance, when a laptop or smartphone performs light tasks, its processor consequently consumes less power by operating at a lower voltage and frequency. Conversely, for intense workloads like gaming or video editing, both voltage and frequency scale up dynamically. This ensures, therefore, that performance needs are adequately met without excessive power draw during idle periods.
Moreover, advanced forms of DVFS exist, such as Adaptive Voltage and Frequency Scaling (AVFS). AVFS uses intelligent algorithms and also employs real-time monitoring for finer adjustments. Typically, on-chip sensors assist by responding to thermal and performance feedback. Companies like Intel and AMD, for example, have successfully implemented DVFS for decades, with notable examples including Intel SpeedStep and AMD PowerNow!
Tackling Escalating Energy Demands with Next-Gen Efficiency Techniques: Enabling Advanced Power Efficiency Design
Modern chips, particularly complex Systems-on-a-Chip (SoCs), often incorporate many functional blocks, each with unique performance requirements. For instance, a critical path needing high speed demands a higher voltage, whereas a non-critical peripheral often works well at a lower voltage. Multi-voltage design specifically leverages this difference. In essence, it divides the chip into distinct “voltage islands,” where each island runs at its lowest possible voltage. Consequently, only high-performance blocks receive higher voltage. This precise control then significantly cuts overall chip power consumption and is a common method in mobile processors, where components like GPUs, CPUs, and modems have widely differing power needs, representing a significant energy saving innovation.
Multi-Threshold CMOS (MTCMOS): Modern Power Optimization for Leakage Reduction
Multi-Threshold CMOS (MTCMOS) technology specifically combats static leakage at a very precise level. Indeed, this sophisticated technique utilizes transistors with varying threshold voltages (Vth), all integrated onto the same chip. On one hand, low-threshold transistors switch faster; however, they leak more current, making them suitable for critical, high-speed paths where performance is paramount. Conversely, high-threshold transistors are slower, but they leak significantly less current, thus making them ideal for non-critical paths where a small performance drop is acceptable for substantial static power savings. Ultimately, this intelligent mix optimizes both speed and leakage characteristics across the entire circuit, a key aspect of advanced power efficiency design.
Advanced Transistor Architectures: Foundations of Advanced Power Efficiency Design
As chips shrink to sub-5nm, planar transistors reach limits. Consequently, leakage control and efficiency improvement become difficult. Therefore, advanced transistor architectures are now standard for advanced power efficiency design.
- FinFET (Fin Field-Effect Transistor): Intel pioneered FinFETs, now widely adopted by foundries like TSMC and Samsung. These 3D transistors feature a gate wrapping the silicon channel. This design, therefore, offers superior control, reducing leakage and boosting switching performance. Consequently, FinFETs are vital for advanced power efficiency design in mobile and high-performance computing.
- Gate-All-Around (GAA) Transistors: As FinFETs approach scaling limits, Gate-All-Around (GAA) transistors emerge as the next step, often utilizing nanosheet or nanowire structures. Crucially, the gate fully surrounds the channel in GAA designs, thereby offering even greater control than FinFETs. This leads to further reduced leakage and significantly improved power efficiency at tiny sizes, with Apple and Qualcomm already incorporating them.
- 3D Chip Stacking: Beyond single transistor advancements, 3D chip stacking (3D-IC) offers a paradigm shift. Specifically, it vertically integrates multiple chip dies, thus inherently shortening data travel. This, in turn, leads to faster communication and lower power use versus 2D layouts. Furthermore, it allows for heterogeneous integration, combining diverse dies like CPUs, memory, or accelerators. Ultimately, therefore, this optimizes both performance and efficiency.
Beyond Silicon: Advanced Power Efficiency Design with Novel Materials
While silicon forms the traditional base of electronics, its inherent limits are now increasingly clear, especially for high-power and high-frequency applications. Consequently, a significant shift is occurring towards wide-bandgap (WBG) materials. These include Gallium Nitride (GaN) and Silicon Carbide (SiC). This revolutionary change, therefore, for power electronics promises huge efficiency gains, highlighting a new era for energy saving innovations.
GaN and SiC possess superior electrical properties compared to silicon. For example, they operate effectively at higher voltages, and furthermore, they skillfully handle higher temperatures and frequencies. This unique combination greatly reduces energy loss, thereby enabling devices to achieve faster switching speeds. Moreover, it significantly improves thermal management; both aspects are vital for efficient power conversion. Ultimately, these materials handle more power in smaller spaces, making them ideal for demanding applications, and are central to modern power optimization.
Exploring GaN and SiC for Energy Saving Innovations
Gallium Nitride (GaN) transistors offer fast switching and low on-resistance, consequently excelling in high-frequency power conversion. As a result, GaN increasingly appears in consumer electronics; for example, fast chargers for phones and laptops (e.g., Anker, RavPower) widely utilize them. Their small size and high efficiency, in turn, allow for smaller, cooler, and more powerful adapters. Furthermore, GaN adoption is growing significantly in data centers and automotive systems.
Silicon Carbide (SiC) devices, conversely, excel in high-voltage, high-power applications. Their exceptional thermal conductivity enables operation at high temperatures without performance drops, thereby ensuring strong reliability. SiC has, in fact, revolutionized electric vehicles (EVs), powering inverters that boost range and speed up charging (e.g., Tesla inverters). Moreover, SiC is crucial for solar inverters (e.g., Enphase, SolarEdge), industrial motor drives (e.g., Siemens, ABB), and critical grid infrastructure.
Ultimately, the widespread adoption of Wide-bandgap (WBG) materials marks a key industry trend. Power engineers increasingly leverage these materials to design significantly better converters and inverters, embodying advanced power efficiency design. These devices, in turn, are not only more efficient but also smaller, lighter, and remarkably more reliable. Furthermore, this innovation cuts operational costs and minimizes carbon footprints across numerous sectors.
Intelligent Optimization: AI and ML for Advanced Power Efficiency Design
AI and Machine Learning (ML) are rapidly growing fields. However, they do much more than simply process data; instead, they are fundamentally transforming energy management by intelligently optimizing consumption. Advanced algorithms, for example, enable AI/ML to create smarter power systems that are both more efficient and resilient across numerous sectors. Indeed, digital technologies, including AI, are projected to offer huge energy savings; specifically, industrial energy use could drop by 10% globally by 2040, thanks to cutting-edge power management techniques.
Optimizing Energy Consumption: Driving Advanced Power Efficiency Design
AI algorithms thoroughly analyze vast amounts of both real-time and historical data. Consequently, they identify complex patterns that help them predict energy demands, thereby fundamentally changing energy management. This intelligent approach, therefore, significantly aids optimization across many diverse environments, including:
- Industrial Facilities: In industrial facilities, ML models optimize machinery, pumps, and ventilation systems. Specifically, they analyze production schedules, sensor readings, and ambient conditions, thus ensuring energy is used only precisely when needed.
- Data Centers: Data centers are notoriously high energy consumers. However, AI significantly aids their efficiency. For example, Google’s DeepMind famously used AI, which cut cooling energy by 40% in its data centers. This, in turn, translated to a 15% overall PUE drop. AI achieved this by accurately predicting load changes and subsequently optimizing fan and pump speeds.
- Smart Buildings: In smart buildings, AI dynamically adjusts integrated systems like HVAC, lighting, and ventilation. It does so by utilizing occupancy data, weather forecasts, time of day, and user preferences. Companies like Siemens and Honeywell, for instance, successfully integrate AI into their building management systems, thereby saving significant energy in homes and businesses.
Data Centers: Advanced Power Efficiency Design in Hardware in Cutting-Edge Power Management
Power plants, data centers, and industrial equipment operate continuously; therefore, their reliability is absolutely crucial. AI/ML consequently enables highly effective predictive maintenance. It does this by analyzing sensor data from these assets, allowing ML models to identify minor anomalies and detect early signs of wear or malfunction. This capability, therefore, enables proactive maintenance, preventing failures, reducing unplanned downtime, extending equipment life, and optimizing maintenance schedules. Ultimately, this approach reduces energy waste from faulty components and significantly cuts emergency repair costs. For example, General Electric successfully uses AI to monitor gas turbines, ensuring optimal performance and predicting necessary maintenance, exemplifying modern power optimization.
Tackling Escalating Energy Demands with Advanced Power Efficiency Design for Next-Gen Efficiency Techniques
AI and ML are vital for modern power grids. Indeed, they effectively optimize entire systems. These technologies manage energy resources in real-time, accurately forecast consumption, and seamlessly integrate renewable sources like solar and wind power. Furthermore, they significantly boost grid security by continuously checking for cyber threats. AI systems, moreover, optimize power plant operations, determine the best electricity dispatch, and route power more efficiently across networks. This ultimately improves fuel efficiency for plants; as a result, utilities experience lower costs (e.g., National Grid), and consumers receive stable, resilient energy. Consequently, smart grid development relies heavily on AI/ML.
Chip miniaturization presents a significant challenge. As nodes shrink past 5nm, leakage control and power density become major concerns. Material limits and quantum effects further add complexity. Therefore, overcoming hurdles necessitates breakthroughs in material science, plus new transistor designs like GAA and innovative packaging for heat and power. These advancements are vital for next-gen efficiency techniques.
Despite challenges, the outlook for advanced power efficiency design remains optimistic, driven by continuous innovation. The future involves greater integration, adaptability, and intelligence. Hardware, software, and AI will increasingly collaborate to optimize energy use. Ultimately, from self-aware chips to smart grids, the goal is clear: a high-performance, sustainable digital world meeting demands while respecting environmental limits.
Holistic Approaches: Sustainable Design and Manufacturing for Advanced Power Efficiency Design
Just as Software-Defined Networking (SDN) transformed network management, similarly, Software-Defined Power (SDP) is revolutionizing power infrastructure. SDP utilizes a software layer that abstracts power resources. This, in turn, allows power delivery to dynamically match changing demands in real-time. Consequently, this innovative approach significantly impacts dynamic settings, including data centers, edge computing, and large industrial operations, all of which benefit immensely from cutting-edge power management.
SDP offers smart, detailed power allocation. Crucially, this removes the need for over-provisioning; consequently, systems no longer require power for theoretical peak demand. Instead, SDP precisely delivers power exactly when and where needed. This, therefore, greatly reduces energy waste and also builds a flexible, programmable power ecosystem. Ultimately, this system intelligently manages IT loads based on power needs, while also considering both cost and environmental factors, advancing advanced power efficiency design.
Energy-Efficient Hardware in Data Centers: The Foundation of Digital Life through Advanced Power Efficiency Design
A key SDP advantage is its dynamic load balancing. Indeed, this automatically distributes power loads across servers, thus preventing hotspots and optimizing power resource use.
Moreover, SDP enables real-time power optimization. It constantly monitors consumption and dynamically adjusts delivery to components. For instance, underused server racks see SDP intelligently scale down power, thus ensuring efficiency.
Furthermore, SDP proactively addresses power quality issues. It identifies and mitigates problems like harmonics or poor power factor, thereby improving reliability and network efficiency.
In addition, SDP fundamentally boosts reliability and resilience. With real-time visibility and control, SDP identifies failures, isolates faults, and reallocates power, maintaining critical operations.
Ultimately, SDP leads to major cost reductions. By optimizing power utilization, it reduces waste and extends equipment life. This, in turn, translates into substantial operational cost savings.
Therefore, SDP represents a significant move towards smarter power management, creating responsive, efficient systems. Moreover, it lays essential groundwork for adaptable digital infrastructures, with HPE and Intel integrating SDP concepts, advancing cutting-edge power management.
Advanced Manufacturing: Energy Saving Innovations from Production
Advanced power efficiency design involves more than simply optimizing current usage; in fact, it also actively generates power from ambient sources. Energy harvesting, for instance, captures tiny amounts of environmental energy and converts this into usable electricity. Consequently, this innovative approach removes the need for traditional batteries, avoiding frequent replacements. As a result, devices can operate autonomously, requiring less maintenance and producing less electronic waste.
These energy harvesting solutions, therefore, particularly suit low-power devices, including widespread sensors and wearables. In such applications, traditional batteries are often impractical, primarily due to limitations concerning size, weight, or maintenance. Thus, energy harvesting offers an ideal and sustainable alternative.
Key Challenges in Advanced Power Efficiency Design
Solar energy harvesting uses small photovoltaic cells, which capture indoor or outdoor light. New materials boost indoor efficiency, with some systems reaching 40%. Consequently, solar power is vital for wireless sensors in smart homes and industrial IoT (e.g., EnOcean).
Thermal energy harvesting employs thermoelectric generators (TEGs), which convert temperature differences into electricity via the Seebeck effect. Consequently, TEGs can capture waste heat, use body heat for wearables, or harness environmental gradients.
Vibrational energy generates power, as piezoelectric materials create charge from stress or vibration. This, therefore, allows devices to harness energy from machine vibrations, human movement, or ambient noise, ultimately powering remote sensors and embedded systems.
Electromagnetic (RF) energy harvesting captures RF from sources like Wi-Fi or broadcast towers, converting it to DC power. Although less efficient, RF nonetheless suits ultra-low-power applications, providing trickle charging for small electronics.
Overall, energy harvesting greatly aids the Internet of Things (IoT), as it enables self-powered devices to run maintenance-free for long periods. Companies like Powercast and Texas Instruments currently lead this field.
Holistic Approaches: Sustainable Design and Manufacturing
Advanced power efficiency design extends beyond individual components and software; instead, it encompasses sustainable design, advanced manufacturing practices, and the integration of renewable energy and intelligent automation in infrastructure. This comprehensive approach, therefore, ultimately aims to create truly sustainable energy ecosystems.
Smart Building Technologies: Intelligent Infrastructure
Buildings are significant energy consumers. Consequently, smart building technologies have emerged specifically to optimize energy usage by employing interconnected systems operating in real-time, embodying contemporary energy reduction strategies.
- Smart Thermostats: Smart Thermostats, such as Google Nest and Ecobee, learn user patterns and subsequently intelligently control HVAC systems. This, in turn, significantly cuts heating and cooling waste.
- Energy Management Systems (EMS): Energy Management Systems (EMS) are platforms that continuously monitor, analyze, and control a building’s energy consumption. They, therefore, provide actionable data and automated adjustments, thus optimizing efficiency, leading to energy saving innovations.
- Automated Lighting Systems: Automated Lighting Systems utilize occupancy sensors, daylight harvesting, and smart scheduling. This, in turn, ensures lights are only on when needed, thereby greatly reducing electricity consumption.
Ultimately, smart buildings dynamically respond to conditions and occupant behavior. This, consequently, drastically cuts energy waste and lowers operational costs. As a result, this approach significantly benefits both commercial and residential properties, further enhancing advanced power efficiency design.
Energy-Efficient Hardware in Data Centers: The Foundation of Digital Life through Advanced Power Efficiency Design
Data centers form the backbone of our digital world. However, they are also enormous energy consumers. Therefore, boosting their power efficiency requires a multi-faceted approach, where energy-efficient hardware is of paramount importance.
- Latest IT Hardware: Upgrading to the latest IT hardware significantly improves efficiency. Modern server processors (e.g., Intel Xeon, AMD EPYC), GPUs (e.g., NVIDIA H100), and storage systems all incorporate advanced chip designs. These designs, for instance, offer smart power management, with key features including dynamic frequency scaling and better thermal characteristics. Ultimately, they deliver more compute per watt, thus shrinking the energy footprint.
- Modular Data Centers: Modular data centers are prefabricated, scalable units specifically designed for energy efficiency from inception. Indeed, they integrate renewable energy and advanced cooling (e.g., direct liquid cooling, free cooling), and also boast optimized airflow. This combination, consequently, makes them highly efficient and quick to deploy.
Companies like Google, Microsoft, and Amazon Web Services invest heavily in custom silicon and innovative cooling. This investment, consequently, drives down the Power Usage Effectiveness (PUE) of their data centers.
Advanced Manufacturing: Efficiency from Production
Manufacturing process innovation significantly boosts power efficiency. Specifically, it uses optimized workflows, reduces waste, and creates lighter, more energy-dense components. Therefore, advanced manufacturing plays a subtle yet vital role in advanced power efficiency design.
- Additive Manufacturing (3D Printing): Additive Manufacturing (3D Printing) builds components layer by layer, thereby using less material waste. This technology, moreover, enables complex shapes and lightweight parts, benefiting aerospace and automotive. Ultimately, lighter parts require less energy to operate (e.g., GE Additive).
- Robotics with Advanced Controls: Modern industrial robots (e.g., KUKA, FANUC) are specifically designed for energy efficiency. They utilize optimized motion planning, regenerative braking, and smart power management. These features, consequently, collectively cut their own energy consumption during tasks.
- Process Optimization: Process optimization fine-tunes manufacturing processes. It does this by using data analytics and simulation, which then reduces idle time, optimizes machine usage, and minimizes energy-intensive steps.
These manufacturing advances ensure sustainable production. Crucially, components themselves are efficiently made, and this directly contributes to the final product’s overall energy efficiency.
Challenges and Future Perspectives
While advanced power efficiency design has improved greatly, significant challenges nevertheless remain, thus necessitating continuous innovation. The explosive growth of AI, for example, presents a key concern, since training complex AI models—like LLMs from OpenAI and Google—demands huge computing power. This, therefore, means a rapidly rising global energy use in data centers.
Currently, data centers consume about 1% of global electricity. However, this figure is projected to rise sharply. Forecasts, for instance, indicate AI growth alone could almost double data center electricity demand by 2026, and potentially increase by 165% by 2030. This significant surge, consequently, places immense strain on current infrastructure, driving the need for more energy saving innovations.
Tackling Escalating Energy Demands
Various solutions ease escalating demand. Better cooling is crucial; consequently, liquid and immersion cooling gain traction as traditional air cooling struggles with dense AI racks. Also, a strong push for alternative energy sources sees tech giants investing in renewables (solar, wind, nuclear). Concurrently, researchers create energy-efficient AI architectures and specialized hardware (Google’s TPUs, NVIDIA GPUs). Moreover, algorithmic changes actively shrink AI’s energy footprint.
Chip miniaturization presents a significant challenge. As nodes shrink past 5nm, leakage control and power density become major concerns. Material limits and quantum effects further add complexity. Therefore, overcoming hurdles necessitates breakthroughs in material science, plus new transistor designs like GAA and innovative packaging for heat and power. These advancements are vital for next-gen efficiency techniques.
Despite challenges, the outlook for advanced power efficiency design remains optimistic, driven by continuous innovation. The future involves greater integration, adaptability, and intelligence. Hardware, software, and AI will increasingly collaborate to optimize energy use. Ultimately, from self-aware chips to smart grids, the goal is clear: a high-performance, sustainable digital world meeting demands while respecting environmental limits.
Conclusion
The journey toward better advanced power efficiency design is dynamic and represents a multifaceted effort, which is key to modern technology’s evolution. For instance, IC design incorporates techniques like DVFS and advanced transistor architectures, such as FinFET/GAA. Furthermore, wide-bandgap materials like GaN and SiC demonstrate immense potential, thus enabling hardware innovation to actively build a more efficient future. Concurrently, AI and ML algorithms intelligently orchestrate power management, making it proactive and predictive, and optimizing consumption across numerous applications. Moreover, Software-Defined Power introduces unprecedented flexibility and control at the infrastructure level.
Energy harvesting, in addition, facilitates autonomous devices. Sustainable practices are increasingly integrated into smart buildings, while advanced manufacturing also significantly contributes. Ultimately, the overall power efficiency landscape is transforming profoundly. Although challenges exist—such as AI’s escalating energy demands and chip miniaturization limits—cross-industry efforts nevertheless promise continuous innovation. Strategically integrating diverse technologies is not merely about cost reduction or compliance; rather, it actively builds a resilient future. Indeed, it makes our electrified world both high-performing and sustainable, and therefore, the relentless pursuit of advanced power efficiency design undeniably unlocks unparalleled technological progress.
Frequently Asked Questions
Dynamic Voltage and Frequency Scaling (DVFS) is crucial for advanced power efficiency design. Specifically, it intelligently adjusts a processor’s voltage and frequency based on its real-time workload. Power consumption is highly sensitive to voltage (V²); therefore, even small voltage drops save significant energy, particularly during light usage. Consequently, DVFS expertly balances performance needs with energy savings, a prime example of modern power optimization.
Gallium Nitride (GaN) and Silicon Carbide (SiC) represent a significant leap forward in advanced power efficiency design. These wide-bandband (WBG) materials outperform silicon because they operate effectively at higher voltages, temperatures, and frequencies. This, in turn, offers faster switching and substantially lower energy losses. Consequently, GaN and SiC are ideal for demanding applications, including EV inverters, solar converters, and fast chargers. Ultimately, this results in smaller, more efficient, and robust power devices, demonstrating next-gen efficiency techniques.
Advanced Software and AI for Energy Optimization
AI and Machine Learning (ML) significantly boost power efficiency through intelligent optimization. AI algorithms, for instance, analyze vast amounts of data, which then helps systems predict and dynamically adjust energy use. For example, AI optimizes data center cooling, fine-tunes HVAC in smart buildings, and also aids predictive maintenance. This collective effort, therefore, prevents energy waste from faulty equipment and optimizes grid management, embodying cutting-edge power management.
Software-Defined Power (SDP) represents another innovative technology that fundamentally transforms power management. Specifically, SDP separates management logic from physical infrastructure; consequently, a software layer intelligently allocates power resources dynamically and in real-time. This, therefore, ensures power is delivered precisely when and where needed, reducing energy waste and improving power quality. Moreover, it boosts reliability and ultimately lowers costs. As a result, SDP is particularly beneficial for data centers and edge computing, showcasing contemporary energy reduction strategies.
Key Challenges in Power Efficiency
Achieving greater advanced power efficiency design faces several significant challenges. Foremost among these is AI’s rapidly escalating energy demands, which drastically increases data center power use. This, in turn, necessitates better cooling solutions, as well as more energy-efficient AI hardware and algorithms. Similarly, chip miniaturization below 5nm proves increasingly tough, since controlling leakage currents and managing power density become major concerns. Therefore, overcoming these hurdles necessitates breakthroughs in material science and also requires advanced transistor designs, such as Gate-All-Around (GAA).
