Automated, high throughput optimization of multistep and cyclic etch and deposition processes using SandBox Studio AI

Kara Kearney; Leandro Medina; Roger T. Bonnecaze; Meghali J. Chopra

doi:10.1117/12.2614284

25 May 2022 Automated, high throughput optimization of multistep and cyclic etch and deposition processes using SandBox Studio AI

Kara Kearney, Leandro Medina, Roger T. Bonnecaze, Meghali J. Chopra

Author Affiliations +

Proceedings Volume 12056, Advanced Etch Technology and Process Integration for Nanopatterning XI; 1205608 (2022) https://doi.org/10.1117/12.2614284
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

The development of new technologies and advanced nodes is capitally intensive due to process design strategies that involve dependent unit processes with different yields and performances. This has led to the exploration of model-based optimization to cut the cost and time of recipe creation; however, computational optimization of semiconductor processes is quite challenging due to multi-dimensional parameter spaces and limited experimental data. SandBox Studio™ AI is a computational tool that automatically builds a hybrid physics-based and machine learning model that can be used to predict optimal process recipes and explore novel process changes such as different incoming mask geometries and step durations. Herein, we show the utilization of SandBox Studio™ AI to build a computational representation of a cyclic etch and deposition process of a high aspect ratio channel etch with the following detrimental effects – bowing, resist over-etching, clogging via deposition, and twisting. The model was calibrated to a synthetic data set of thirteen experiments with five varying process parameters. Then, an optimal recipe was predicted that minimized the observed detrimental effects. The model was then used to explore different incoming mask geometries and step durations to improve the recipe even further. This capability is made possible by the software’s foundational physics-based model and is not possible using conventional statistics and machine learning based tools.

Conference Presentation

Citation Download Citation

Kara Kearney, Leandro Medina, Roger T. Bonnecaze, and Meghali J. Chopra "Automated, high throughput optimization of multistep and cyclic etch and deposition processes using SandBox Studio AI", Proc. SPIE 12056, Advanced Etch Technology and Process Integration for Nanopatterning XI, 1205608 (25 May 2022); https://doi.org/10.1117/12.2614284

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