# Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization

arXiv: https://arxiv.org/abs/2304.14408 · HTML: https://mlsanity.com/paper/2304.14408/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 78 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots](https://mlsanity.com/paper/2203.06967/) (2022)
- [CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image](https://mlsanity.com/paper/2203.13009/) (2022)
- [SIOD: Single Instance Annotated Per Category Per Image for Object Detection](https://mlsanity.com/paper/2203.15353/) (2022)
- [Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU](https://mlsanity.com/paper/2012.07489/) (2020)
- [A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation](https://mlsanity.com/paper/2105.13553/) (2021)

## Built on by (most cited first, 3 of 3)

- [Transferable polychromatic optical encoder for neural networks](https://mlsanity.com/paper/2411.02697/) (2024)
- [A Self-Supervised Robotic System for Autonomous Contact-Based Spatial Mapping of Semiconductor Properties](https://mlsanity.com/paper/2411.09892/) (2024)
- [Closed-Loop Robotic Manipulation of Transparent Substrates for Self-Driving Laboratories using Deep Learning Micro-Error Correction](https://mlsanity.com/paper/2512.06038/) (2025)

## Abstract (arXiv)

High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, with the boom in development of high-throughput synthesis tools that champion production rates up to 104 samples per hour with flexible form factors, most sample characterization methods are either slow (conventional rates of 101 samples per hour, approximately 1000x slower) or rigid (e.g., designed for standard-size microplates), resulting in a bottleneck that impedes the materials-design process. To overcome this challenge, we propose a set of automated material property characterization (autocharacterization) tools that leverage the adaptive, parallelizable, and scalable nature of computer vision to accelerate the throughput of characterization by 85x compared to the non-automated workflow. We demonstrate a generalizable composition mapping tool for high-throughput synthesized binary material systems as well as two scalable autocharacterization algorithms that (1) autonomously compute the band gap of 200 unique compositions in 6 minutes and (2) autonomously compute the degree of degradation in 200 unique compositions in 20 minutes, generating ultra-high compositional resolution trends of band gap and stability. We demonstrate that the developed band gap and degradation detection autocharacterization methods achieve 98.5% accuracy and 96.9% accuracy, respectively, on the FA1-xMAxPbI3, 0≤ x ≤ 1 perovskite semiconductor system.