Semiconductor And Beyond Newsletter - #199

Author

Chetan Arvind Patil
March 24, 2024

General Purpose GPUs (GPGPUs) represent a significant evolution in computing, transforming GPUs from specialized hardware focused solely on graphics processing to versatile computing units capable of handling a broad range of tasks. This shift has enabled GPUs to play a crucial role in areas beyond gaming and graphics, including scientific research, data analysis, and artificial intelligence (AI).

By leveraging their parallel processing capabilities, GPGPUs can execute complex mathematical and data-intensive operations at speeds vastly superior to traditional CPUs, making them indispensable for tasks requiring high computational throughput.

Read more....

THREAD

 Robotics play a pivotal role in semiconductor manufacturing, handling tasks like silicon wafer material handling, mask making, FOSB packing/unpacking, equipment inspection, and more. These robotic solutions must comply with ISO 14644 standards. Key companies in this domain include ABB, KUKA, FANUC, Boston Dynamics, Mitsubishi Electric, and Kawasaki Robotics. The emergence of AGI-powered robots is poised to revolutionize the industry further, pushing towards a 'lights-out' manufacturing approach where robots operate with minimal human intervention. This evolution signifies a major leap in efficiency and precision in semiconductor production, promising significant advancements in technology and manufacturing capabilities.

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India is on the brink of a significant expansion in the semiconductor manufacturing sector, expected to generate numerous private sector jobs in the next three years, diversifying beyond the limited roles previously available mainly in public institutes. This growth introduces a range of new job roles spanning semiconductor fabrication, testing, assembly/packaging, equipment support, and comprehensive design and manufacturing, marking the start of a long-term employment boom in the industry.

This development presents a golden opportunity for Indian students, alongside the challenge of acquiring the necessary skills in a rapidly evolving field. With the educational system yet to adapt, aspiring professionals must take proactive steps towards self-learning through reading, internships, networking with industry experts, and leveraging online resources and open-source information on semiconductor manufacturing.

While certification courses may offer a route to formal recognition of skills, the emphasis should be on practical, hands-on experience, given that a significant portion of learning occurs on the job. This approach ensures that students are not only prepared to enter the semiconductor manufacturing industry but are also adaptable to its future advancements and innovations.

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India's venture into semiconductor manufacturing, aiming to produce its first wafer by 2026, marks a significant leap towards enhancing its role in the global tech landscape. This ambitious project, while promising, carries its set of benefits and challenges.

Pros:

  • Localization: Fabricating 28nm silicon products within India will boost domestic tech innovation and reduce reliance on international fabrication facilities.

  • Supply Chain Diversification: India's entry into semiconductor manufacturing offers an alternative in the global supply chain, potentially reducing bottlenecks.

  • Employment Opportunities: The initiative will create diverse skilled jobs, from engineering to manufacturing roles, contributing to the country's economic growth.

  • Cost-Effectiveness: Domestic fabrication provides a cost-efficient solution for companies seeking 28nm manufacturing capabilities without their own fabs.

  • Ecosystem Development: This move is a crucial step towards establishing a comprehensive semiconductor manufacturing ecosystem in India.

Cons

  • Market Challenges: Entering the market and acquiring multinational customers will require overcoming significant hurdles.

  • Operational Hurdles: The initial phase will involve extensive learning around product qualification and yield optimization, which could present unexpected challenges.

  • Competition: India will face stiff competition from established semiconductor manufacturing nations, making it hard to achieve targeted production volumes.

  • Talent Acquisition: Building a skilled workforce will be time-consuming, with training being a critical component of the early stages.

  • R&D Investment: Balancing the ramp-up of R&D efforts with the financial goal of reaching a break-even point necessitates substantial initial investment in research and development.

Despite these challenges, the hurdles faced by India's semiconductor fabrication initiative are not unique and can be overcome with strategic planning and execution. The anticipation around this development is heightened by the presence of a major industry in India, already a significant market for 28nm technology, hinting at a ready domestic customer base that could drive the success of this venture. This step forward not only represents a milestone for India but also signals its readiness to play a pivotal role in the global semiconductor industry.

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The collaboration between NVIDIA, the Chinese University of Hong Kong, and Hong Kong Baptist University has led to a significant advancement in the field of computational lithography, crucial for semiconductor manufacturing. Their joint research has culminated in the release of a paper titled "LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing," which also marks the open-sourcing of a valuable dataset. This development is set to accelerate the progress of AI-driven computational lithography techniques.

LithoBench aims to elevate the benchmarking of AI applications in computational lithography, providing over 120,000 circuit layout tiles from real designs and synthesized inverse lithography technology (ILT) test cases. It introduces a framework designed to assist in the design and evaluation of deep neural networks for the purposes of lithography simulation and mask optimization, complete with extensive guidelines for setup, dataset utilization, and model training/testing processes.

With the entire LithoBench project made open source, accessible via GitHub, it presents an excellent opportunity for researchers and practitioners in the fields of lithography, semiconductors, computational mathematics, and algorithms. This initiative not only fosters further research in AI-enhanced computational lithography but also signifies a promising research avenue for those at the intersection of mathematics and semiconductor technology.

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The semiconductor industry is charting a new course towards achieving More-Than-Moore capabilities, this time through the introduction of "Ghiplet" – a portmanteau of GPU and chiplet. This innovative concept underscores a significant shift towards modular GPU architectures, where GPUs are constructed from multiple specialized blocks rather than being designed as a single, monolithic entity. This modular approach offers enhanced flexibility and scalability in GPU design, meeting diverse performance needs across various application scenarios, particularly in AI computing.

Ghiplets allow leading companies like AMD and NVIDIA to tailor GPUs more precisely for different market segments, ranging from high-end AI computing solutions to low-power solutions suitable for mobile and edge computing. The realization of Ghiplet architectures necessitates the development of advanced interconnect technologies to facilitate efficient, high-speed communication among the Ghiplet components. Key innovations in this domain, such as silicon photonics and advanced packaging techniques, are imperative to achieving the low latency and high bandwidth required for top-tier GPU applications.

The advent of Ghiplets represents an exciting development in the fields of AI, silicon design, and semiconductor manufacturing, promising to deliver more customized, powerful, and efficient computing solutions. This approach not only reflects the ongoing evolution within the semiconductor industry but also highlights the relentless pursuit of overcoming the limitations imposed by traditional scaling laws, opening up new possibilities for technological advancement and application.

VLOG

This week's vlog explores the transformative concept of Ghiplet in GPU architecture, spotlighting how AI advancements are reshaping the designs of industry giants like Intel, AMD, and NVIDIA. Ghiplet technology departs from traditional monolithic GPU designs in favor of a modular approach, assembling GPUs from specialized units tailored for AI applications.

This shift addresses manufacturing efficiencies, reduces waste, and enhances production by using smaller, manageable components. The advancement in interconnect technologies, such as silicon photonics, is crucial for the efficient communication between Ghiplets, promising GPUs that are more powerful, efficient, and adaptable to AI's evolving demands, marking a significant milestone in semiconductor innovation and AI-driven computing.

TOOLS

LithoBench dataset is a collection of circuit layout tiles for deep-learning-based lithography simulation and mask optimization. LithoBench consists of more than 120k tiles that are cropped from real circuit designs or synthesized according to the layout topologies of famous ILT testcases.

The ground truths are generated by a famous lithography model in academia and an advanced ILT method. Based on the data, we provide a framework to design and evaluate deep neural networks (DNNs) with the data.

The framework is used to benchmark state-of-the-art models on lithography simulation and mask optimization. We hope LithoBench can promote the research and development of computational lithography.

CONNECT

Whether you are a student with the goal to enter semiconductor industry (or even academia) or a semiconductor professional or someone looking to learn more about the ins and outs of the semiconductor industry, please do reach out to me.

Let us together explore the world of semiconductor and the endless opportunities:

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