M01 Applications of Machine Learning in Semiconductor Manufacturing and Test

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Date: 
2019-03-25
Time: 
14:00-18:00
Location / Room: 
TBA

Organisers

Haralampos-G. Stratigopoulos, Sorbonne Université, CNRS, LIP6, FR (Contact Haralampos-G. Stratigopoulos)
Yiorgos Makris, The University of Texas at Dallas, US (Contact Yiorgos Makris)

Throughout the lifetime of an integrated circuit, a wealth of data is collected for ensuring its robust and reliable operation. Ranging from design-time simulations to process characterization monitors, and from high-volume specification tests to diagnostic measurements on customer returns, the information inherent in this data is invaluable. Mining this information using machine learning methods has seen intense interest and numerous breakthroughs in recent years. This tutorial seeks to elucidate the utility of machine learning in semiconductor manufacturing and test. Relevant concepts from machine learning will be introduced, agglomerated with current practice, and showcased using industrial data. Recommendations for practitioners will also be given.

Agenda

TimeLabelSession
13:30M01.1Tutorial and Conference Registration
14:00M01.2Tutorials start
14:00M01.3Introduction and Motivation

Speakers:
Haralampos-G. Stratigopoulos, Sorbonne Université, CNRS, LIP6, FR, Contact Haralampos-G. Stratigopoulos
Yiorgos Makris, The University of Texas at Dallas, US, Contact Yiorgos Makris


Part I will motivate the need, the challenges, and the benefits of using machine learning and will discuss its utility on actual test- and yield-related industrial problems. We will give an abstract representation of problems that can be tackled using machine learning. We will also illustrate the link between machine learning and semiconductor manufacturing and test.

14:20M01.4Overview of Machine Learning Applications in Semiconductor Manufacturing and Test

Speaker:
Yiorgos Makris, The University of Texas at Dallas, US, Contact Yiorgos Makris


Part II will provide a concise and comprehensive overview of applications of machine learning in semiconductor manufacturing and test. For each application, we will define the problem, we will explain how machine learning can come to the rescue, and we will show a case study on industrial datasets. Applications include: alternate test for analog/mixed-signal/RF ICs, test compaction, fault diagnosis, yield learning, post-manufacturing tuning, outlier detection, adaptive test, wafer-level spatial & lot-level spatiotemporal correlation modeling, analog test metrics estimation, neuromorphic on-chip testers, hotspot detection, board-level fault diagnosis, trimming, die inking, pre-silicon verification and post-silicon validation, yield estimation in fab-to-fab migration, yield estimation when transitioning from one design generation to the next.

15:30M01.5Coffee Break for Tutorials
16:00M01.6Recommendations for Practitioners

Speaker:
Haralampos-G. Stratigopoulos, Sorbonne Université, CNRS, LIP6, FR, Contact Haralampos-G. Stratigopoulos


Part III will illustrate the main practical issues when applying machine learning techniques. It will provide several recommendations based on the presenters' own experience in developing several applications in the past. Practical issues that will be discussed include: types of learning machines, feature extraction, feature selection, training and validation processes, dataset preparation, limited and unbalanced datasets, non-stationary datasets, metrics for generalization error, mitigating the generalization error, explainable artificial intelligence.

16:45M01.7Selected Applications in Depth

Speakers:
Haralampos-G. Stratigopoulos, Sorbonne Université, CNRS, LIP6, FR, Contact Haralampos-G. Stratigopoulos
Yiorgos Makris, The University of Texas at Dallas, US, Contact Yiorgos Makris


Part IV will describe in more detail selected applications of machine learning in semiconductor manufacturing and test. We will delve into the following four mainstream applications: alternate test for analog/mixed-signal/RF ICs, adaptive test, yield learning, and hotspot detection. For each application we will discuss the collection of training data, the choice of learning models, the training procedures, etc., and we will provide several cases studies on actual industrial data.

17:45M01.8Emerging Applications

Speaker:
Haralampos-G. Stratigopoulos, Sorbonne Université, CNRS, LIP6, FR, Contact Haralampos-G. Stratigopoulos


Part V will discuss emerging applications. In particular, we will discuss whether deep learning methods open new opportunities for solving efficiently test and semiconductor manufacturing problems. We will also discuss the "inverse" problem of testing machine learning hardware. In particular, we will discuss to what extent testing machine learning hardware is any different from testing any regular integrated circuit. We will also discuss fault tolerance methods that gain interest thanks to the integration of machine learning hardware in autonomous vehicles and systems.

18:00M01.9Tutorials end
18:00M01.10Welcome Reception & PhD Forum