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Proceedings Paper

A method to accelerate creation of plasma etch recipes using physics and Bayesian statistics
Author(s): Meghali J. Chopra; Rahul Verma; Austin Lane; C. G. Willson; Roger T. Bonnecaze
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Paper Abstract

Next generation semiconductor technologies like high density memory storage require precise 2D and 3D nanopatterns. Plasma etching processes are essential to achieving the nanoscale precision required for these structures. Current plasma process development methods rely primarily on iterative trial and error or factorial design of experiment (DOE) to define the plasma process space. Here we evaluate the efficacy of the software tool Recipe Optimization for Deposition and Etching (RODEo) against standard industry methods at determining the process parameters of a high density O2 plasma system with three case studies. In the first case study, we demonstrate that RODEo is able to predict etch rates more accurately than a regression model based on a full factorial design while using 40% fewer experiments. In the second case study, we demonstrate that RODEo performs significantly better than a full factorial DOE at identifying optimal process conditions to maximize anisotropy. In the third case study we experimentally show how RODEo maximizes etch rates while using half the experiments of a full factorial DOE method. With enhanced process predictions and more accurate maps of the process space, RODEo reduces the number of experiments required to develop and optimize plasma processes.

Paper Details

Date Published: 21 March 2017
PDF: 8 pages
Proc. SPIE 10149, Advanced Etch Technology for Nanopatterning VI, 101490X (21 March 2017); doi: 10.1117/12.2263507
Show Author Affiliations
Meghali J. Chopra, The Univ. of Texas at Austin (United States)
Rahul Verma, The Univ. of Texas at Austin (United States)
Austin Lane, The Univ. of Texas at Austin (United States)
C. G. Willson, The Univ. of Texas at Austin (United States)
Roger T. Bonnecaze, The Univ. of Texas at Austin (United States)

Published in SPIE Proceedings Vol. 10149:
Advanced Etch Technology for Nanopatterning VI
Sebastian U. Engelmann, Editor(s)

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