Identifying ill tool combinations via Gibbs Sampler for semiconductor manufacturing yield diagnosis

In semiconductor manufacturing, all up-to-date tool commonality analysis (TCA) algorithms for yield diagnosis are based on greedy search strategies, which are naturally poor in identifying combinational factors. When the root cause of product 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 product 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 this paper. Simulation and field data validation results show that the proposed TCA algorithm performs well in identifying the ill tool combination.