Deep Learning for Rapid Analysis of Spectroscopic Ellipsometry Data. Issue 12 (23rd September 2021)

• Record Type: Journal Article
• Authors: Li, Yifei; Wu, Yifeng; Yu, Heshan; Takeuchi, Ichiro; Jaramillo, Rafael
• Is Part Of: Advanced photonics research. Volume 2:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-23
• Subjects: deep learning -- high-throughput -- phase-change materials -- spectroscopic ellipsometry; Photonics -- Periodicals; 621.36505
• DOI: 10.1002/adpr.202100147
• Languages: English
• ISSNs: 2699-9293

Abstract

Abstract : High‐throughput experimental approaches to rapidly develop new materials require high‐throughput data analysis methods to match. Spectroscopic ellipsometry is a powerful method of optical properties characterization, but for unknown materials and/or layer structures the data analysis using traditional methods of nonlinear regression is too slow for autonomous, closed‐loop, high‐throughput experimentation. Herein, three methods (termed spectral, piecewise, and pointwise) of spectroscopic ellipsometry data analysis based on deep learning are introduced and studied. After initial training, the incremental time for inferring optical properties can be a thousand times faster than traditional methods. Results for multilayer sample structures with optically isotropic materials are presented, appropriate for high‐throughput studies of thin films of phase‐change materials such as GeSbTe (GST) alloys. Results for studies on highly birefringent layered materials are also presented, exemplified by the transition metal dichalcogenide MoS2 . How the materials under test and the experimental objectives may guide the choice of analysis methods are discussed. The utility of our approach is demonstrated by analyzing data measured on a composition spread of GeSbTe phase‐change alloys containing 177 distinct compositions, and identifying the composition with optimal phase‐change figure of merit in only 1.4 s of analysis time. Abstract : Spectroscopic ellipsometry is a powerful and data‐rich metrology to characterize materials, devices, and manufacturing processes. However, traditional data analysis tends to be too slow for rapid feedback. Deep learning methods are developed to quickly and accurately analyze spectroscopic ellipsometry data. The efficacy of the methods are demonstrated using data from high‐throughput synthesis of phase‐change materials for photonics.