There are many techniques for analyzing IC fails, but they are scattered over the professional IC test and diagnosis literature, and in various statistics and data mining handbooks. Moreover, many data mining techniques from other data analysis environments are appropriate for analyzing IC fails, but have not yet been employed for that purpose. Data Mining and Diagnosing IC Fails brings together a large number of analysis techniques that are suitable for IC fail data, but that are not available elsewhere in a single place. Several of the techniques, in fact, have been presented only recently in technical conferences. The various approaches discussed in this book have a thorough theoretical underpinning, but are geared towards applications on real life fail data and state of the art ICs. The description techniques and analysis routines are sufficiently detailed that professional manufacturing engineers can implement them in their own work environment.

Makes various data mining and diagnostic techniques available in one place to professionals The techniques described in this book are not available in one place, and many are not even available in any book Presents a new diagnosis technique SLAT -- single location at a time Includes supplementary material: sn.pub/extras

Inhalt

Introduction.- Statistics.- Yield Statistics.- Area Dependence of the Yield.- Statistics of Embedded Object Fails.- Fail Commonalities.- Spatial Patterns.- Test Coverage and Test Fallout.- Logic Diagnosis.- Slat Based Diagnosis.- Data Collection Requirements.- Appendix A. Distribution of IC Fails.- Appendix B. General Yield Model.- Appendix C. Simplified Center-Satellite Model.- Appendix D. Quadrat Analysis.- Appendix E. Cell Fail Probabilities.- Appendix F. Characterization Group.- Appendix G. Component Fail Probabilities.- Appendix H. Yield and Coverage.- Appendix I. Estimating First Fail Probabilities from the Fallout.- Appendix J. Identity of M and S.- References.- Index.