A method to accelerate creation of plasma etch recipes using physics and Bayesian statistics

Meghali J. Chopra; Rahul Verma; Austin Lane; C. G. Willson; Roger T. Bonnecaze

doi:10.1117/12.2263507

21 March 2017 A method to accelerate creation of plasma etch recipes using physics and Bayesian statistics

Meghali J. Chopra, Rahul Verma, Austin Lane, C. G. Willson, Roger T. Bonnecaze

Proceedings Volume 10149, Advanced Etch Technology for Nanopatterning VI; 101490X (2017) https://doi.org/10.1117/12.2263507
Event: SPIE Advanced Lithography, 2017, San Jose, California, United States

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.

Citation Download Citation

Meghali J. Chopra, Rahul Verma, Austin Lane, C. G. Willson, and Roger T. Bonnecaze "A method to accelerate creation of plasma etch recipes using physics and Bayesian statistics", Proc. SPIE 10149, Advanced Etch Technology for Nanopatterning VI, 101490X (21 March 2017); https://doi.org/10.1117/12.2263507

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available