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Parameter Quantification for Predictive Feature Scale Modeling
Nathan Marchack and Jane Chang, UCLA

Abstract

As novel materials continue to be incorporated into IC devices at ever decreasing length scales, plasma etching faces new challenges yet remains a viable candidate for transferring features with high fidelity. This work aims to demonstrate a robust, predictive kinetics-based model for plasma etching at surfaces and within features, using the etching of complex oxides in halogen based chemistries as examples. The model can be readily expanded to other material and plasma systems, providing a versatile tool for future nanoscale material processing.