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

2025/07/07 by Sameera Bharadwaja H., Siddhrath Jandial, H., Sameera Bharadwaja +6
Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2507.10564

openalex publication_date 2025/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
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