Date/Time
Date(s) - 05/27/2025 - 05/31/2030
12:00 AM
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Instructor
Dr. Li-C Wang is a professor at ECE department at the University of CA, Santa Barbara.
Learning Objectives
Machine learning is a powerful tool for analyzing and making predictions from data. This course introduces the fundamental concepts of machine learning and provides hands-on experience with training and evaluating machine learning models in the context of microelectronic manufacturing problems. The course covers traditional machine learning, such as Nearest Neighbors, LDA, QDA, Naive Bayes, Decision Trees, and Support Vector Machines (SVMs), as well as modern deep learning model fundamentals as far as Convolutional Neural Networks (CNNs). The course also includes multiple case studies of selected applications of machine learning to the microelectronics domain.
Machine learning represents a class of algorithms that assimilate large quantities of data to tune an internal model, then can generalize to data not seen in their given dataset (learning.) In the context of microelectronics, machine learning algorithms can assist with the analysis of large datasets, such as wafer maps, to identify patterns and trends that are difficult to discern with traditional statistical methods. The machine learning methods presented in this course are divided into three modules:
- Unit 1: Overview of Machine Learning: Establishes the foundational perspective and views for understanding application of machine learning concepts to the domain-specific problems in the microelectronics domain. Introduces fundamental traditional machine learning algorithms, such as Nearest Neighbors, LDA, QDA, Naive Bayes, and Decision Trees.
- Unit 2: Domain Specific Machine Learning in Microelectronic Field: Focuses on the fitting error vs model complexity tradeoff in the generalization of traditional machine learning approaches, showing how they bridge to modern Deep Learning techniques. Then evaluates case-studies of these ML approaches in the microelectronics domain, both in lecture and homework.
- Unit 3: Unsupervised Learning in Microelectronic Field: Introduces Unsupervised Learning’s fundamental differences from the supervised techniques covered up to this point, framing the challenges of previous techniques in the pipeline of microelectronic device manufacturing. Then deeply explores representative techniques in unsupervised learning and the risks and benefits of application in microelectronics’ domain-specific context.
- Unit 4: (Supplemental) Machine Learning in Silicon Data Analytics: Reviews the benefits and challenges of machine learning overall in application within the microelectronic domain with specific research case studies and techniques developed by the UCSB research team supplying the course content.
Prerequisites:
- An understanding of high-level programming (loops, objects, functions, libraries) and basic college-level mathematics (algebra, linear algebra, trigonometry, derivatives) is assumed.
- Knowledge of the Python programming language and the fundamental units of microelectronics fabrication (wafers, die, lithography) is helpful but not required.
Biography
Li-C. Wang is a professor at ECE department at the University of CA, Santa Barbara. He received PhD in 1996 from the University of Texas, Austin, and was previously with Motorola PowerPC Design Center. Starting from 2003, his research has focused on investigating how machine learning could be utilized in design and test flows, where he had published more than 100 papers and supervised 22 PhD theses on related subjects. Before that, his research spanned across multiple topics in EDA and test, including microprocessor test and verification, statistical timing analysis, defect-oriented testing, and SAT solvers. He received 10 Best Paper Awards and 2 Honorable Mentioned Paper Awards from major conferences, including recent best paper awards from ITC 2022, ITC 2020, VTS 2016, and VLSI-DAT 2019. He is the recipient of the 2010 Technical Excellence Award from Semiconductor Research Corporation (SRC) for his research contributions in data mining for test and validation. He is the recipient of the 2017 IEEE-TTTC Bob Madge Innovation Award. He is an IEEE fellow and served as the General Chair of the International Test Conference (ITC) in 2017, 2018, and 2023.
