Predicting and optimizing etch recipes for across the wafer uniformity

Meghali Chopra; Roger T. Bonnecaze; Yang Ban; Sofia Helpert

doi:10.1117/12.2515097

20 March 2019 Predicting and optimizing etch recipes for across the wafer uniformity

Meghali Chopra, Roger T. Bonnecaze, Yang Ban, Sofia Helpert

Proceedings Volume 10963, Advanced Etch Technology for Nanopatterning VIII; 1096309 (2019) https://doi.org/10.1117/12.2515097
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

Uniformity of critical dimensions (CDs) across a wafer is an increasing challenge as both CDs and tolerances shrink. Plasma etch uniformity is achieved in part through reactor design and in part through the operating conditions or process recipe of the reactor. The identification of a recipe for a specific etch process is time consuming and expensive, requiring extensive experiments and metrology. Here we present two modules in SandBox StudioTM, SB-Bayesian and SBNeuralNet, to accelerate the prediction and optimization of etch recipes for across the wafer uniformity. A model of etch rates across the wafer is created that accounts for injector locations, gas flow rates and distribution and plasma powers. Synthetic experiments on etching line-space patterns on 300 mm wafers are performed and the CDs and their variations are computed at several hundred site locations. SB-Bayesian requires many fewer experiments to be calibrated and achieve an excellent qualitative match with the experimental data. SB-NeuralNet achieves comparable levels of accuracy to SBBayesian at predicting average CDs and uniformity, but it does not perform as well at predicting trends across the wafer. It is shown that neural nets require a prohibitive amount of experimental data to successfully predict wafer patterns. SBBayesian and SB-NeuralNet were used to create detailed process maps across the parameters space of interest to identify optimal recipes to achieve required CDs and tolerances. Both modules can predict optimal recipe conditions for achieving identified target CD and uniformity metrics. Using these tools, etch recipes for across the wafer uniformity are rapidly optimized at lower cost.

Conference Presentation

Citation Download Citation

Meghali Chopra, Roger T. Bonnecaze, Yang Ban, and Sofia Helpert "Predicting and optimizing etch recipes for across the wafer uniformity ", Proc. SPIE 10963, Advanced Etch Technology for Nanopatterning VIII, 1096309 (20 March 2019); https://doi.org/10.1117/12.2515097

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