Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop

TL;DR

This paper presents a Bayesian optimization framework that uses computer vision to quickly tune inkjet printer hardware for high-throughput semiconductor material experiments, significantly reducing setup time.

Contribution

It introduces a novel computer vision-driven Bayesian optimization method for rapid hardware tuning of inkjet printers in material discovery workflows.

Findings

Converged to optimal hardware conditions in 10 minutes.

Outperformed stochastic gradient descent in tuning efficiency.

Reduced resource and time expenditure for system setup.

Abstract

High-performance semiconductor optoelectronics such as perovskites have high-dimensional and vast composition spaces that govern the performance properties of the material. To cost-effectively search these composition spaces, we utilize a high-throughput experimentation method of rapidly printing discrete droplets via inkjet deposition, in which each droplet is comprised of a unique permutation of semiconductor materials. However, inkjet printer systems are not optimized to run high-throughput experimentation on semiconductor materials. Thus, in this work, we develop a computer vision-driven Bayesian optimization framework for optimizing the deposited droplet structures from an inkjet printer such that it is tuned to perform high-throughput experimentation on semiconductor materials. The goal of this framework is to tune to the hardware conditions of the inkjet printer in the shortest amount of time using the fewest number of droplet samples such that we minimize the time and resources spent on setting the system up for material discovery applications. We demonstrate convergence on optimum inkjet hardware conditions in 10 minutes using Bayesian optimization of computer vision-scored droplet structures. We compare our Bayesian optimization results with stochastic gradient descent.