Machine Learning Modeling and Run-to-Run Control of an Area-Selective Atomic Layer Deposition Spatial Reactor

被引：3

作者：

Tom, Matthew ^{[1
]}

Wang, Henrik ^{[1
]}

Ou, Feiyang ^{[1
]}

Orkoulas, Gerassimos ^{[2
]}

Christofides, Panagiotis D. ^{[1
,3
]}

机构：

[1] Univ Calif Los Angeles, Dept Chem & Biomol Engn, Los Angeles, CA 90095 USA

[2] Widener Univ, Dept Chem Engn, Chester, PA 19013 USA

[3] Univ Calif Los Angeles, Dept Elect & Comp Engn, Los Angeles, CA 90095 USA

来源：

COATINGS | 2024年 / 14卷 / 01期

基金：

美国国家科学基金会;

关键词：

run-to-run control; semiconductor manufacturing; area-selective atomic layer deposition; multiscale modeling; machine learning;

D O I：

10.3390/coatings14010038

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Semiconducting materials require stringent design specifications that make their fabrication more difficult and prone to flaws that are costly and damaging to their computing and electrical properties. Area-selective atomic layer deposition is a process that addresses concerns associated with design imperfections but requires substantial monitoring to ensure that process regulation is maintained. This work proposes a run-to-run controller with an exponentially weighted moving average method for an area-selective atomic layer deposition rotary reactor by adjusting the rotation speed of the substrate to control the growth per cycle of the wafer, which is calculated through a multiscale model with machine learning integration for pressure field generation and kinetic Monte Carlo simulations to increase computational efficiency. Results indicate that the run-to-run controller was able to bring the process to the setpoint when subjected to moderate pressure and kinetic shift disturbances.

引用

页数：18