Identifying Tool Combinations Critical to Semiconductor Manufacturing Yield with Gibbs Sampler
Date Issued
2012
Date
2012
Author(s)
Hsu, Yu-Chin
Abstract
In semiconductor manufacturing, the soundness of tool commonality analysis (TCA) technique has a high impact on the effectiveness of product yield diagnosis. However, all up-to-date TCA algorithms are based on greedy search strategies, which are naturally poor in identifying combinational root causes. When the root cause of wafer yield loss is tool combination instead of a single tool, the greedy-search-oriented TCA algorithm usually results in both high false and high miss identification rates.
As the feature size of semiconductor devices continuously shrinks down, the problem induced by greedy-search-oriented TCA algorithm becomes severer because the total number of tools is getting large and wafer yield loss is more likely caused by a specific tool combination. To cope with the tool combination problem, a new TCA algorithm based on Gibbs Sampler, a Markov Chain Monte Carlo (MCMC) stochastic search technique, is proposed. In specific, a tool health indicator with binary value is defined for each tool to determine if it should be involved in the tool combination as root cause.
With the Gibbs Sampler, the computation complexity is reduced from O(2n) to about O(n2), where n is the number of tools. Simulation and field data validation results show that the proposed TCA algorithm performs well in identifying the ill tool combination.
Subjects
Semiconductor Manufacturing Yield Analysis
Tool Commonality Analysis
Tool Combination
Markov Chain Monte Carlo
Gibbs Sampler
Type
thesis
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