Atomic Layer Deposition Optimization via Targeted Adaptive Design.

Marieme Ngom, Carlo Graziani, Noah Paulson

Keywords: Gaussian Pocesses, Atomic Layer Deposition, Bayesian Optimization, Targeted Adaptive Design

Abstract:

Atomic Layer Deposition (ALD) is a commonly employed process for producing
conformal nanoscale coatings in the microelectronics and energy materials industries.
ALD processes are composed of cycles of sequential self-limiting chemical
reactions followed by purges with an inert gas to produce atomically thin coatings.
At the end of each cycle, the Growth Per Cycle (GPC) which corresponds to net
mass or thickness change from the previous ALD cycle is determined. Optimizing
ALD processes for stable and uniform GPC for a new combination of reactants
is challenging as the optimal combination of gas timings, temperature, and gas
partial pressures spans a large multidimensional space and in-situ characterization
is typically performed with a limited number of mass sensors. In this work, we use
Targeted Adaptive Design (TAD), a Gaussian Process (GP)-based probabilistic
machine learning framework that aims at efficiently and autonomously locating
control parameters that would yield a desired target within specified tolerance, to
optimize simulated ALD processes.

Submission Number: 126