Fast and accurate prediction of process variation band with custom kernels extracted from convolutional networks

Gangmin Cho; Taeyoung Kim; Youngsoo Shin

doi:10.1117/12.2658307

28 April 2023 Fast and accurate prediction of process variation band with custom kernels extracted from convolutional networks

Gangmin Cho, Taeyoung Kim, Youngsoo Shin

Author Affiliations +

Proceedings Volume 12495, DTCO and Computational Patterning II; 124951Z (2023) https://doi.org/10.1117/12.2658307
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Abstract

Process variation band (PVB) is important for a number of lithography applications such as yield estimation, hotspot detection, and so on. It is derived through multiple lithography simulations of a mask pattern while optical settings such as dose and focus are varied. Quick estimation of PVB has been studied. A simple approach assumes optical settings for innermost and outermost PVB contour; it requires only two simulations, but the assumption of such optical settings does not always hold. We postulate that two sets of good custom kernels exist; one set for lithography simulation to extract outermost PVB contour, and the other for innermost PVB contour. Since lithography simulation can be mapped to a convolutional neural network (CNN) with kernels corresponding to convolution filters, each set can be obtained by training corresponding CNN with a number of sample reference contours. Our experiments indicate that the average intersection over union (IoU) between reference- and predictedPVBs reaches 97% with 0 PBVs having IoU smaller than 50%. This can be compared to the state-of-art of PVB prediction using conditional generative adversarial networks (cGANs), where average IoU is only 89% with 12 PBVs having IoU smaller than 50%.

Citation Download Citation

Gangmin Cho, Taeyoung Kim, and Youngsoo Shin "Fast and accurate prediction of process variation band with custom kernels extracted from convolutional networks", Proc. SPIE 12495, DTCO and Computational Patterning II, 124951Z (28 April 2023); https://doi.org/10.1117/12.2658307

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