Intelligent Photolithography Corrections Using Dimensionality Reductions

• Published in: IEEE photonics journal Vol. 11; no. 5; pp. 1 - 15
• Main Authors: Parashar, Parag, Akbar, Chandni, Rawat, Tejender S., Pratik, Sparsh, Butola, Rajat, Chen, Shih H., Chang, Yung-Sung, Nuannimnoi, Sirapop, Lin, Albert S.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive optics; Algorithms; Artificial intelligence; Computation; Diffractive imaging; Dimensionality reduction; Error correction; Factor analysis; Image detection; Independent component analysis; Integrated circuits; Intelligent manufacturing systems; Lithography; Machine learning; Masks; Optical distortion; Optical imaging; Photolithography; Resists; Run time (computers); Singular value decomposition; Technologies for computing
• ISSN: 1943-0655; 1943-0655; 1943-0647
• DOI: 10.1109/JPHOT.2019.2938536

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).