Название: Machine Learning Support for Fault Diagnosis of System-on-Chip
Автор: Patrick Girard, Shawn Blanton, Li-C. Wang
Издательство: Springer
Год: 2023
Страниц: 320
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB

This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.

Today’s electronic systems are composed of complex Systems on a Chip (SoCs) made of heterogeneous blocks that comprise memories, digital circuits, analog and mixed-signal circuits, etc. To fit a critical application standard requirement, SoCs pass through a comprehensive test flow (functional, structural, parametric, etc.) at the end of the manufacturing process. The goal is to achieve near-zero Defective Parts per Million (DPPM) so as to ensure the quality level required by the standard. Unfortunately, imperfections in the manufacturing process may introduce systematic defects, especially when the first devices are produced while the process is not yet mature. Identification of these systematic defects and correction of the related manufacturing process call for efficient diagnosis techniques. Hence, the goal of diagnosis is to extract information from test data in order to identify the nature and the causes of defects that have occurred in a SoC. Note that additional data can also be produced and used to improve the diagnosis process, such as distinguishing test patterns used only during the diagnosis phase.

However, with the fast development and vast application of Machine Learning (ML) in recent years, ML-based techniques have been shown to be highly valuable for diagnosis. They can be used for volume diagnosis after manufacturing to improve production yield or for diagnosis of customer returns to identify any possible systematic degradation patterns. The main advantage of ML-based diagnosis techniques is that they can deal with huge amount of insightful test data that could not be efficiently exploited otherwise in a reasonable amount of time.

The first chapter gives some prerequisites on fault diagnosis. Basic terms, such as defect, fault, and failure, are first enumerated. Then, basic concepts of test and fault simulation are described.

Chapter 2 is dedicated to the presentation of conventional methods for fault diagnosis. The chapter focuses on the automated tools and methods along with design features at the architectural, logic, circuit, and layout level that are needed to facilitate silicon debug and defect diagnosis of integrated circuits. These design features are generally referred to as design for debug and diagnosis (DFD).

The third chapter provides details of Machine Learning techniques proposed so far to solve various VLSI testing problems. It focuses on explaining scope of Machine Learning in VLSI testing. First, it gives a high-level overview of Machine Learning. After that, it describes the types of Machine Learning algorithms. Then, it explains some popular and commonly used Machine Learning algorithms. After that, this chapter discusses some recent Machine Learning based solutions proposed to solve VLSI testing problems. Finally, it discusses the strength and limitations of these methods.

Chapter 4 is dedicated to machine learning support for logic diagnosis and defect classification. After a preliminary discussion about attempts to distinguish maleficent defects from benign variations, the chapter presents Machine Learning techniques developed so far for distinguishing variations from reliability threats due to defects.

The fifth chapter is dedicated to Machine Learning in logic circuit diagnosis. It is organized into three main sections that describe the use of ML for pre-diagnosis, during-diagnosis, and post-diagnosis, so as to characterize when and how a given methodology enhances the classic outcomes of diagnosis that include localization, failure behavior identification, and root cause of failure.