Tool-to-Tool Matching Analysis Based Difference Score Computation Methods for Semiconductor Manufacturing
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913940840120320 |
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| author | H., Sameera Bharadwaja Jandial, Siddhrath Agashe, Shashank S. Moore, Rajesh Kumar Reddy Kim, Youngkwan |
| author_facet | H., Sameera Bharadwaja Jandial, Siddhrath Agashe, Shashank S. Moore, Rajesh Kumar Reddy Kim, Youngkwan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10564 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tool-to-Tool Matching Analysis Based Difference Score Computation Methods for Semiconductor Manufacturing H., Sameera Bharadwaja Jandial, Siddhrath Agashe, Shashank S. Moore, Rajesh Kumar Reddy Kim, Youngkwan Machine Learning Artificial Intelligence Signal Processing 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. |
| title | Tool-to-Tool Matching Analysis Based Difference Score Computation Methods for Semiconductor Manufacturing |
| topic | Machine Learning Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2507.10564 |