M10 Test and diagnosis: Board-level functional fault diagnosis: industry needs and research solutions

Agenda

Time	Label	Session
14:30	M10.1	Motivation and Background Panelist: Bill Eklow, Cisco Systems, Inc., US The introductive part of the tutorial will provide the motivation for board-level fault diagnosis and describe state-of-the-art in industry practice. It will also highlight the difficult problems being faced today that require new solutions. More in details, topics to be covered include: Structural and functional tests Rationale for board diagnosis and repair Review of available EDA tools and commercial solutions (including IEEE standards) Why system and ATE tests do not correlate Introduction to NTFs and troubleshooting NTFs Open problems and call to action.
15:30	M10.2	Data-driven diagnosis and guidance for repair Panelist: Luca Cassano, Politecnico di Milano, IT This part of the tutorial will first introduce the basic concepts of adaptive incremental functional diagnosis (AIFD) and it will discuss the main features that an automated AIFD approach should offer to the test/diagnosis engineer. Then, several machine learning techniques that can be exploited for AIFD will be introduced. More in details, the basic concepts related to the theory and application of the following machine learning techniques would be introduced: Decision Trees Data Mining Bayesian Belief Networks Support Vector Machines k-Nearest Neighbors Artificial Neural Networks The results of a comparative analysis of the previously presented machine learning techniques applied to AIFD would be shown. Moreover, the three best approaches, i.e., the data mining-, the Bayesian belief network- and the decision trees-based ones, would be presented in details. Finally, a new statistical AIFD approach would be discussed. Results of the application of all the presented approaches to both synthetic and real-world electronic boards would be shown
17:00	M10.3	How to handle data overload, evaluate diagnosis systems, and accomplish diagnosis at early stages of product manufacturing? Panelist: Krishnendu Chakrabarty, Duke University, US The accuracy of diagnosis and time needed for diagnosis also depend on the quality of syndromes (erroneous observations). Redundant or irrelevant syndromes not only lead to long diagnosis time, but also increase diagnosis complexity. This last part of the tutorial will review recent advances in enhancing diagnostic accuracy using various means, and include the following topics: Information-theoretic measures Ranking of syndromes and ambiguity analysis for test-set redesign Knowledge discovery and knowledge transfer Industry case studies

Time

Label

Session

14:30

M10.1

Motivation and Background

Panelist:
Bill Eklow, Cisco Systems, Inc., US

The introductive part of the tutorial will provide the motivation for board-level fault diagnosis and describe state-of-the-art in industry practice. It will also highlight the difficult problems being faced today that require new solutions. More in details, topics to be covered include:

Structural and functional tests
Rationale for board diagnosis and repair
Review of available EDA tools and commercial solutions (including IEEE standards)
Why system and ATE tests do not correlate
Introduction to NTFs and troubleshooting NTFs
Open problems and call to action.

15:30

M10.2

Data-driven diagnosis and guidance for repair

Panelist:
Luca Cassano, Politecnico di Milano, IT

This part of the tutorial will first introduce the basic concepts of adaptive incremental functional diagnosis (AIFD) and it will discuss the main features that an automated AIFD approach should offer to the test/diagnosis engineer. Then, several machine learning techniques that can be exploited for AIFD will be introduced. More in details, the basic concepts related to the theory and application of the following machine learning techniques would be introduced:

Decision Trees
Data Mining
Bayesian Belief Networks
Support Vector Machines
k-Nearest Neighbors
Artificial Neural Networks

The results of a comparative analysis of the previously presented machine learning techniques applied to AIFD would be shown. Moreover, the three best approaches, i.e., the data mining-, the Bayesian belief network- and the decision trees-based ones, would be presented in details. Finally, a new statistical AIFD approach would be discussed. Results of the application of all the presented approaches to both synthetic and real-world electronic boards would be shown

17:00

M10.3

How to handle data overload, evaluate diagnosis systems, and accomplish diagnosis at early stages of product manufacturing?

Panelist:
Krishnendu Chakrabarty, Duke University, US

The accuracy of diagnosis and time needed for diagnosis also depend on the quality of syndromes (erroneous observations). Redundant or irrelevant syndromes not only lead to long diagnosis time, but also increase diagnosis complexity. This last part of the tutorial will review recent advances in enhancing diagnostic accuracy using various means, and include the following topics:

Information-theoretic measures
Ranking of syndromes and ambiguity analysis for test-set redesign
Knowledge discovery and knowledge transfer
Industry case studies