HPC Application on Long-Life-Cycle Tool

It's been a while haven't generated new articles. Thanks to my good friend, Drew F. Due to the recent visit, he brought up this interesting question to me. What would you generate the sourcing/supply strategy for HPC which is used for semiconductor inspection tools and the life cycle of such a tool may last more than 25 years? This article aims to provide a series of strategic thoughts and tries to solve this issue. At the beginning of the article, I will explain what HPC is, what the critical factors of HPC are, and the challenges of HPC.

High-performance computing (HPC) refers to using advanced computational resources to process large datasets and solve complex problems much faster than standard computing systems. HPC systems are often composed of interconnected computing servers, forming clusters or grids that work together to increase processing power. This collective computing power allows HPC to tackle intensive tasks such as simulations, financial modeling, drug discovery, weather forecasting, image computing, and large-scale data analysis that would overwhelm typical personal computers or small-scale servers. HPC is measured in terms of floating-point operations per second (FLOPS), with some systems reaching petaFLOPS (quadrillions of operations per second) or even exaFLOPS (quintillions), which is a scale used by the world’s fastest supercomputers. Whether used by researchers, corporations, or governments, HPC has become a cornerstone for fields requiring immense computational precision and speed.

The critical factors driving the effectiveness of HPC systems include processing power, storage capacity, network speed, and scalability. Processing power is usually achieved through parallel computing, where multiple processors (CPUs or GPUs) work simultaneously to divide and conquer tasks. High-end processors, especially those designed for specific tasks like scientific simulations or deep learning, are essential for achieving the high speeds required for HPC. Storage capacity is another critical aspect, as HPC systems must manage, store, and access vast quantities of data efficiently. This demands the use of fast storage solutions like SSDs, as well as large-scale storage architectures that ensure data is readily available when needed. Network speed and bandwidth also play a crucial role in HPC, as data must be transferred quickly between computing nodes to avoid bottlenecks. A high-throughput network ensures that each part of the system can communicate efficiently, allowing for smooth processing and avoiding delays that would diminish overall performance. Scalability, or the ability to expand the system by adding more processors or storage without sacrificing performance, is also essential for HPC systems to grow in capacity as needed.

However, there are several significant challenges associated with deploying and maintaining HPC systems. Cost is one of the foremost barriers, as building a powerful HPC infrastructure requires a significant financial investment in hardware and the accompanying infrastructure such as cooling systems and physical space. Additionally, complexity in configuration and management makes HPC difficult to set up and maintain, requiring specialized IT knowledge and experience to optimize system performance and avoid downtime. Energy consumption is another critical challenge, as HPC systems consume vast amounts of power to operate the computing clusters and cooling systems. Ensuring energy efficiency has become a key area of focus, as large HPC installations, particularly at supercomputing centers, have a sizable environmental impact. Scalability also poses a challenge; as HPC systems grow, maintaining efficiency in data handling, job scheduling, and resource allocation becomes more difficult, which can introduce new technical problems. Finally, software optimization is a growing concern, as not all software can fully utilize HPC’s parallel architecture, meaning that specialized software development is often required to unlock the full potential of the system.

Till now, you should have a basic understanding of HPC. Now let's translate the original question: "What would you generate the sourcing/supply strategy for HPC which is used for semiconductor inspection tools and the life cycle of such a tool may last more than 25 years?" to the following issue agenda:

1. Hardware Obsolescence:

• Outdated Components: Over a 25-year span, HPC hardware (servers, processors, storage, and network equipment) will most likely become obsolete multiple times. Replacement components may become unavailable, and newer hardware might not be fully compatible with the existing system, leading to significant integration challenges.
• Increased Maintenance Costs: As hardware ages, maintaining and sourcing parts becomes increasingly expensive and difficult, which could lead to higher operating costs and more frequent system downtime.

My suggestion: To prepare HPC components for the long life cycle of a semiconductor inspection tool, it's essential to analyze the lifecycle of the key components—such as processors, storage, and network equipment—over time, which is conduct a comprehensive component lifecycle analysis. In the initial phase, typically covering the first five years, most components are widely available, with manufacturers offering stable lead times, MOQ, and clear pricing structures. This period provides an opportunity to establish strong supplier relationships, lock in long-term pricing agreements, and understand vendor product roadmaps. However, as the tool enters the midlife phase (5-15 years), components may begin to face end-of-life (EOL) or obsolescence issues. It becomes critical to monitor the market for EOL notices and explore alternative options, such as newer generations or refurbished components. Stock levels need to be carefully managed during this time to avoid both overstocking and shortages, as each carries distinct risks. Overstocking can lead to financial waste, while shortages could result in operational disruptions.

In the later stages of the tool’s lifecycle (15-25 years), sourcing components becomes increasingly difficult as manufacturers phase out older technologies. Prices may rise due to scarcity, and suppliers may require minimum order quantities that exceed the actual demand for aging systems. By this point, it may be necessary to look for components on the secondary market or explore refurbished parts, depending on their availability and reliability. It is crucial during this phase to negotiate favorable terms with vendors for long-term stock agreements to ensure the continued availability of essential components. A clear inventory management strategy, aligned with forecasts and usage patterns, will help maintain optimal stock levels without incurring unnecessary costs.

To navigate the challenges of lead times, MOQ, and price fluctuations, a proactive approach to vendor management is key. Monitoring price trends and conducting regular benchmarking will ensure the pricing remains reasonable and competitive. Fixed-price agreements can help mitigate the risk of sharp price increases as components become obsolete. Lead times should be closely tracked throughout the lifecycle, particularly for critical components where delays could impact tool performance. Ensuring that suppliers are transparent about their EOL timelines and exploring alternative sourcing options are vital steps to maintaining a stable supply chain. This comprehensive approach will enable better preparation for the long-term maintenance of HPC components, ensuring the tool remains functional throughout its extended life cycle.

2. Technological Advancements

• Lagging Behind Innovation: Semiconductor technology evolves rapidly, and a 25-year-old tool could be far behind the current state of innovation. Keeping such a tool relevant would require regular upgrades or retrofitting, but over time, the tool's architecture may no longer support the latest technological advances in HPC or inspection capabilities.
• Incompatibility with Modern Systems: Emerging technologies, such as AI-driven analysis or quantum computing, may not be compatible with a decades-old tool, limiting its future utility or forcing costly overhauls.

My suggestion: To resolve the issue of technological advancements, a modular design approach can be adopted early on. For those who are not familiar with modular design can refer to my prior article: (https://www.dhirubhai.net/pulse/product-modularization-supply-chain-1-rob-chang/?trackingId=q9BxU5VJTRSyBCdSTduA9Q%3D%3D) By designing the tool with modularity in mind, key components—such as processors, storage, and network systems—can be easily upgraded without requiring an overhaul of the entire system. This strategy allows individual components to be swapped out as more advanced technologies become available, enabling the tool to remain current with the latest HPC innovations. Regular upgrades can be scheduled in alignment with product roadmaps from vendors, ensuring that the tool's architecture remains adaptable to new developments in semiconductor technology. Additionally, partnering with suppliers who offer scalable solutions will make it easier to integrate newer hardware and software without major disruptions.

For incompatibility with modern systems, especially with the rise of AI-driven analysis or quantum computing, the tool's software architecture must be built to support future-proofing. Implementing a software platform that allows for backward compatibility ensures that newer technologies can be integrated without requiring a full redesign of the system. Furthermore, utilizing open standards (Open Compute Project/OCP https://www.opencompute.org/ can be a good direction for investment) and interoperable protocols for communication between components will enable the tool to adapt to emerging technologies without being locked into legacy systems. This ensures that as new AI models or quantum processing methods become relevant, they can be incorporated into the tool's functionality. Periodic software updates and integration with modern platforms will allow the system to stay in sync with evolving computational methods, reducing the risk of obsolescence.

Another key resolution is to create long-term partnerships with industry leaders and HPC vendors who are at the forefront of HPC innovation. These partnerships can provide insights into upcoming technological shifts, enabling the tool's architecture to anticipate changes and plan upgrades accordingly. By working with suppliers who are committed to supporting legacy systems while offering cutting-edge advancements, the tool can stay relevant throughout its life cycle. Additionally, collaboration with research institutions or participation in industry consortiums can ensure that the tool evolves in step with new technologies, reducing the likelihood of being left behind by innovation.

3. Energy Efficiency and Environmental Concerns:

• Rising Energy Costs: Older HPC systems tend to be less energy-efficient than newer models. Over a 25-year life cycle, this inefficiency will contribute to higher operational costs and a larger environmental footprint, making the system less sustainable.
• Cooling and Power Requirements: The tool's infrastructure, including cooling systems, may become inadequate as technology evolves. Meeting energy and cooling demands for outdated hardware over two decades could pose a significant operational challenge.

My suggestion: An essential strategy is to implement energy-efficient upgrades and retrofits at regular intervals throughout the tool’s lifecycle. Since newer HPC components tend to be more energy-efficient, swapping out aging processors, memory, and storage systems with more modern equivalents can significantly reduce energy consumption. Additionally, installing energy monitoring and management software can help identify areas where energy use is excessive, allowing operators to optimize system settings and performance to achieve better energy efficiency. In parallel, adopting power-saving techniques, such as dynamic voltage and frequency scaling (DVFS), can further reduce power consumption by adjusting the power usage based on workload demand. These strategies can help mitigate rising operational costs and decrease the environmental footprint, making the system more sustainable over its life cycle.

For cooling and power requirements, it is crucial to ensure that the tool’s infrastructure is designed to scale alongside technological advancements. Older cooling systems may struggle to keep up with the heat output of outdated, inefficient components, leading to both performance and safety issues. Upgrading the cooling system to modern, energy-efficient solutions—such as liquid cooling or high-efficiency air cooling—can better manage the heat generated by the HPC system over time. Another option is to implement smart cooling technologies, which dynamically adjust cooling levels based on real-time heat production, ensuring that the system uses only the necessary amount of energy for cooling without overloading the infrastructure.

Additionally, planning for redundant power supply systems and integrating renewable energy sources where possible can help address long-term power demands. Shifting towards renewable energy integration, like solar or wind power, can reduce dependency on traditional energy grids and stabilize operational costs. Implementing uninterruptible power supply (UPS) systems can further ensure that power fluctuations or outages don’t disrupt operations, while also contributing to energy efficiency. By adopting these approaches, the tool's infrastructure can adapt to evolving cooling and power needs, ensuring reliable operation while keeping energy and operational costs in check.

4. Supply Chain and Spare Parts Availability:

• Dwindling Supply Chain Support: Manufacturers of key components (processors, storage, etc.) may discontinue production or shift focus to more advanced technologies, making it harder to procure replacement parts or technical support. This could lead to extended downtimes or force premature retirement of the tool.
• Vendor Lock-in Risks: Long-term reliance on specific vendors for components or services might expose the tool to risks if those vendors change business models, cease operations, or discontinue support for legacy systems.

My suggestion: One strategy is to secure long-term supply agreements (LTSA) with MULTIPLE vendors (Dual or multiple sources are highly recommended) early in the tool's lifecycle. These agreements can include clauses for continued support, guaranteed spare parts availability, or access to second-generation components, ensuring that essential parts remain accessible even as technologies evolve. Establishing relationships with secondary markets and refurbishment services is also critical. These sources can provide replacement parts when original manufacturers discontinue production, minimizing the risk of extended downtimes due to component shortages. Additionally, stockpiling critical parts that are prone to obsolescence and regularly assessing inventory needs can help mitigate potential supply chain disruptions.

To mitigate vendor lock-in risks, it is important to adopt a multi-vendor strategy from the outset. Diversifying suppliers reduces reliance on any single vendor, thereby lowering the risk of disruption if one supplier changes business models or discontinues support for legacy systems. When designing the HPC system, ensure that the architecture supports interoperability and open standards, which allows for flexibility in choosing components from different vendors. This approach prevents the tool from being tied to proprietary solutions that limit future upgrade paths. In addition, regular benchmarking and market evaluations will help identify emerging vendors and alternatives, ensuring a steady flow of support and avoiding premature tool retirement.

Another key strategy is to maintain close communication with suppliers about their long-term product roadmaps. Understanding the lifecycle of the components they provide will enable more strategic planning for future upgrades and replacements. In parallel, it’s important to evaluate the possibility of custom-built components or solutions developed in collaboration with vendors. These custom solutions may provide longer-term support, as vendors may offer extended service agreements or tailor their offerings to the unique needs of the tool. By adopting a combination of these approaches, the risks of supply chain disruption and vendor lock-in can be minimized, ensuring continued access to critical spare parts and technical support throughout the tool's 25-year lifecycle.

5. Evolving User Requirements:

• Shifting Business Needs: Although people say Moore's law (The number of transistors in an integrated circuit (IC) doubles about every two years) is dying. We still see cutting edge technology coming out each year. Over long product life cycle, user requirements and operational processes in the semiconductor industry will likely evolve, potentially outpacing the tool's capabilities. Keeping the tool flexible and adaptable to new inspection methods or integration with other advanced systems will become increasingly difficult.

My suggestion: To resolve the challenge of shifting business needs, a flexible and scalable system design is critical. By building the tool with modularity and scalability in mind, key components—such as computing power, storage, and networking—can be upgraded or expanded as business requirements evolve. For example, as demand for higher performance or increased capacity arises, individual modules can be replaced or enhanced without requiring a complete overhaul of the system. This modular approach allows the tool to adapt to changes in technology, production volume, or inspection complexity, ensuring it remains relevant and aligned with evolving business goals.

Additionally, implementing software-driven solutions allows the tool to stay agile in response to changing business needs. Adopting virtualization or cloud integration can enable the system to scale up or down based on computational demands, making it easier to adjust capacity in response to shifts in production or inspection requirements. Cloud-based platforms, in particular, can offer flexibility in deploying advanced analytics or integrating new technologies like AI-driven inspection methods without being tied to the hardware constraints of a decades-old tool. This approach not only ensures that the tool can handle new business demands but also enables easier integration with other advanced systems as the industry evolves.

To ensure long-term alignment with business needs, regular assessments of business and technological trends should be conducted. By establishing a continuous feedback loop between operations, business strategy, and IT teams, the tool can be periodically reviewed to assess how well it meets current and future business objectives. This enables proactive planning for upgrades or modifications, rather than reactive changes in response to market demands. Building in flexibility and establishing regular reviews allows the tool to remain adaptable and responsive to shifting business environments over its extended lifecycle.

In conclusion, the long product life cycle presents substantial challenges in hardware maintenance, software support, energy efficiency, and adaptability. Addressing these challenges will require careful planning, a modular design approach, and regular upgrades to prevent the tool from becoming obsolete or too costly to maintain.