Brock Estel Osborn

Diagnostic effectiveness in computer systems using deterministic random ESD

A new criterion for electrostatic discharge (ESD) testing was recently developed where qualification effectiveness for a given apparatus is shown to be a function of the coverage obtained through ESD event coincidence with the diagnostic software/hardware in use. The authors show the effectiveness of ESD testing on a large-scale computer system undergoing testing in a typical manufacturing environment using both the present, industry accepted, deterministic ESD unit and an ESD unit based upon the recently established criterion. In addition, they provide guidelines for maximizing error detection and fault isolation as a function of ESD events and diagnostic software coverage. They further provide results showing the fallacy of assuming that a deterministic ESD unit will eventually cover every segment of a similarly deterministic diagnostic routine given unconstrained runtime. They also show the attainment of 100% diagnostic coverage in all cases based upon a 0.9 confidence using a randomly exercised ESD unit.<<ETX>>

Statistical modeling in manufacturing: adapting a diagnostic tool to real-time applications

This paper describes a process for constructing a statistical model to automate the analysis of data from complex diagnostic tools. The method is demonstrated on data taken from an optical emission spectrometer (OES), one of the most powerful tools used in semiconductor manufacturing for detecting the chemical composition and impurity levels in plasma processes. The analysis of OES data currently requires hours of manual effort by an expert spectroscopist, rendering it ineffective for real-time monitoring and control. However, through the use of statistical modeling, the analysis can be performed automatically on a personal computer in a matter of seconds. The process of model construction is examined in general, and methods are developed for demonstrating how information from an expert can be combined with information from the data in order to provide a statistical basis for analysis. The effectiveness of the model is demonstrated on data from typical plasma processes.

Computer aided reliability modeling and applications in semiconductor manufacturing

Abstract We present a computerized reliability model based on evolving Markov processes. The model has the capability to self-organize and adopt. The differences between knowledge engineering methods and the proposed model are also discussed.