DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Gangmin | ko |
dc.contributor.author | TAEYOUNG, KIM | ko |
dc.contributor.author | Shin, Youngsoo | ko |
dc.date.accessioned | 2023-08-09T05:01:46Z | - |
dc.date.available | 2023-08-09T05:01:46Z | - |
dc.date.created | 2022-11-22 | - |
dc.date.created | 2022-11-22 | - |
dc.date.issued | 2023-03-01 | - |
dc.identifier.citation | DTCO and Computational Patterning II 2023 | - |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | http://hdl.handle.net/10203/311319 | - |
dc.description.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 predicted-PVBs 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%. | - |
dc.language | English | - |
dc.publisher | SPIE | - |
dc.title | Fast and Accurate Prediction of Process Variation Band with Custom Kernels Extracted from Convolutional Networks | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85164133132 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | DTCO and Computational Patterning II 2023 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | San Jose, California | - |
dc.identifier.doi | 10.1117/12.2658307 | - |
dc.contributor.localauthor | Shin, Youngsoo | - |
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