SEMI Teams with Cornell University to Accelerate Technology Development Using Machine Learning and AI
SEMI announced a research and development (R&D) project to speed technology progress and problem-solving in microelectronics manufacturing and across the supply chain by driving new efficiencies using machine learning (ML) and artificial intelligence (AI). Under an agreement with SEMI, Cornell University will optimize and accelerate two critical process steps – lithography and plasma etch. Supported through SEMI’s R&D program with the U.S. Army Research Laboratory (ARL), the project aims to help accelerate the adoption of data-driven AI methodologies to streamline microelectronics operations.
The project will lay the foundation for SEMI to establish data transfer and management standards crucial to the trusted exchange of trade secrets, IP and other sensitive information. New standards and protocols are vital as the industry moves into the era of big data.
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Semiconductor manufacturing is extremely complex due to the intricate interdependencies among various processes, environments, tools, and materials. This complexity deepens with the rise of new technologies and makes existing analytical approaches such as statistical process control (SPC) and design of experiments (DOE) more challenging. In addition, analyzing cleanroom data is now harder because of varying formats and lack of physical models for categorizing the data. Consequently, identifying root causes of manufacturing problems is much more difficult, slowing the development of new technologies.
“Streamlining manufacturing processes across the microelectronics supply chain to address the rising complexity of technology development is key for the industry to keep pace with market demand and drive growth,” said Ajit Manocha, SEMI president and CEO. “The microelectronics industry has been slow to adopt ML and AI due, in part, to IP and data security concerns that impede data-sharing. SEMI’s agreement with Cornell, supported by ARL, is an important step in using ML and AI techniques to tackle this and other issues and bring new technologies to market faster.”
Tools and materials from several SEMI members will be used for the project at Cornell University’s NanoScale Science & Technology Facility (CNF).
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The budget for the year-long project is provided by the ARL grant, SEMI, and through in-kind contributions from companies in the form of materials and testing services. The principal investigators from Cornell University are Dr. Christopher K. Ober, professor of Materials Engineering and Director of the CNF, and Dr. Amit Lal, professor in the School of Electrical and Computer Engineering. Dr. Peter Doerschuk, Dr. Ed Suh, and Dr. Don Tennant will also support the project with expertise spanning computer systems, high-performance software and facilities management.
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