標題: Intelligent Photolithography Corrections Using Dimensionality Reductions
作者: Parashar, Parag
Akbar, Chandni
Rawat, Tejender S.
Pratik, Sparsh
Butola, Rajat
Chen, Shih H.
Chang, Yung-Sung
Nuannimnoi, Sirapop
Lin, Albert S.
交大名義發表
電子工程學系及電子研究所
National Chiao Tung University
Department of Electronics Engineering and Institute of Electronics
關鍵字: Lithography;Diffractive imaging;Technologies for computing
公開日期: 1-十月-2019
摘要: With the shrinking of the IC technology node, optical proximity effects (OPC) and etch proximity effects (EPC) are the two major tasks in advanced photolithography patterning. Machine learning has emerged in OPC/EPC problems because conventional optical-solver-based OPC is time-consuming, and there is no physical model existing for EPC. In this work, we use dimensionality reduction (DR) algorithms to reduce the computation time of complex OPC/EPC problems while the prediction accuracy is maintained. Also, we implement a pure machine learning approach where the input masks are directly mapped to the output etched patterns. While one photolithographic mask can generate many experimental patterns at once, our pure ML-based approach can shorten the trial-and-error period in the photolithographic correction. Additionally, we demonstrate the automation in SEM images preprocessing using feature detection, and this facilitates intelligent manufacturing in semiconductor processing. The input vector dimensions are effectively reduced by two orders of magnitude while the observed mean squared error is not affected significantly. The computation runtime is reduced from 4804 s of the baseline calculation to 10 s-200 s The MSE values changed from the baseline 0.037 to 0.037 for singular value decomposition (SVD), to 0.039 for independent component analysis (ICA), and to 0.035 for factor analysis (FA).
URI: http://dx.doi.org/10.1109/JPHOT.2019.2938536
http://hdl.handle.net/11536/153050
ISSN: 1943-0655
DOI: 10.1109/JPHOT.2019.2938536
期刊: IEEE PHOTONICS JOURNAL
Volume: 11
Issue: 5
起始頁: 0
結束頁: 0
顯示於類別:期刊論文