Tool-to-Tool Matching Analysis Based Difference Score Computation Methods for Semiconductor Manufacturing

TL;DR

This paper introduces novel analysis methods for tool-to-tool matching in semiconductor manufacturing, effectively handling heterogeneous equipment and avoiding reliance on static data or golden references.

Contribution

It proposes new analysis pipelines that utilize variance and mode-based metrics, demonstrating high correlation with equipment mismatches and extending applicability to diverse equipment types.

Findings

Univariate methods achieve >0.95 correlation with variance

Multivariate methods achieve >0.75 correlation with univariate metrics

Proposed methods are effective without static configuration data or golden references

Abstract

We consider the problem of tool-to-tool matching (TTTM), also called, chamber matching in the context of a semiconductor manufacturing equipment. Traditional TTTM approaches utilize static configuration data or depend on a golden reference which are difficult to obtain in a commercial manufacturing line. Further, existing methods do not extend very well to a heterogeneous setting, where equipment are of different make-and-model, sourced from different equipment vendors. We propose novel TTTM analysis pipelines to overcome these issues. We hypothesize that a mismatched equipment would have higher variance and/or higher number of modes in the data. Our best univariate method achieves a correlation coefficient >0.95 and >0.5 with the variance and number of modes, respectively showing that the proposed methods are effective. Also, the best multivariate method achieves a correlation coefficient >0.75 with the top-performing univariate methods, showing its effectiveness. Finally, we analyze the sensitivity of the multivariate algorithms to the algorithm hyper-parameters.