18-663 Advanced Data Analytics and Machine Learning for Semiconductor Industry
12 units, Carnegie Mellon University
Leading edge semiconductor companies are currently handling several TB of data per day and are only able to actively deal with a fraction of this data stream. There is a huge demand for systems to process the data as rapidly as possible to make quick diagnostic or wafer disposition decisions with minimum human intervention, and also to avoid storing these huge quantities of raw data. This requires comprehensive data analytics systems to cover the entire IC manufacturing supply chain from the frontend wafer manufacturing to the fully packaged systems. The enormous complexity and data volumes require not only creation of such systems but also training a really sophisticated workforce for the rapidly expanding onshore semiconductor manufacturing industry. Many leading USbased companies such as Intel and GlobalFoundries for logic chips, Micron Technology for DRAM and 3d Flash memories, as well as Texas Instruments for analogembedded products and Analog Devices for analog, mixedsignal and digital signal processing chips, are planning huge investments to expand their fabrication facilities throughout the United States. The CHIPS Act promises very significant government investment in expanding the US role in semiconductor manufacturing and it is aimed at restoring the US leadership role in this crucial industry segment that enables progress in virtually all key segments of the countrys economy. This course emphasizes the Machine Learning algorithms to analyze the massive data coming from the fabrication process to provide process control and failure diagnosis. The goal of this course is to prepare students to enter the job market with the necessary skills to handle the data analytics needs in these semiconductor companies. The course will feature several guest lecturers from industry and leading research universities.
Prerequisites: none listed
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