A New Statistical Method for Automatic Partitioning Tools According to Engineers’ Tolerance Control in Process Improvement
|關鍵字:||貝氏分析;資料探勘;RJMCMC;CART;量率提升;製程能力指標;APC;Bayesian fit;data mining;reversible jump Markov chain Monte Carlo;CART;yield enhancement;process capability;APC|
In the semiconductor industry, tool comparison is a key task in the yield and the product quality enhancements. We developed a new method, called tolerance control partitioning (TCP), to automatically partition tools into several homogenous groups based on the related metrology results. This methodology is based on a hierarchical normal model and the implementation is carried out using a Bayesian approach. There are several advantages of using the TCP method. First, it takes into account the unbalanced usage of the tools in the manufacturing processes. Moreover, the “engineer’s tolerance control” can be incorporated into the TCP method via the specification of the priors in the Bayesian analysis, which justifies the significant difference between groups according to the experts’ knowledge. This specification not only has the advantage of adjusting the number of partition groups but also avoids the problem of having too many partition groups with small differences which is often encountered in the conventional approaches. Some simulation results illustrate the advantages of the TCP method compared to the method of classification and regression trees (CART). Moreover, the TCP method is applied to two real examples for the yield and Cp/Cpk enhancement in the semiconductor industry. Both results confirm the practical usefulness of the proposed method. For general applications, the TCP method is also useful for other similar problems such as the comparisons between several experimental recipes or the comparisons between different materials.
|Appears in Collections:||Thesis|
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