Fast Level-Set-Based Inverse Lithography Algorithm for Process Robustness Improvement and Its Application

Geng, Zhen; Zheng, Shuang; Yan, Xiaolang; Luo, Kai-Sheng; Pan, Weiwei

doi:10.1007/s11390-015-1549-7

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…Level set representation for 2D geometrical data has been successfully applied to many important computational physics problems, 22 and it has also been introduced into inverse lithography technologies (ILTs) 23 – 29 In particular, level set data representation has been used for machine learning ILT, such as DevelSet, which learns ILT internal algorithm 28 . The level set function used in ILT is the signed distance function (SDF) defined on a certain spatial grid.…”

Section: Geometrical Data Encoding Scheme and A General Machine Learn...mentioning

confidence: 99%

Can optical proximity correction solution be learned? The learning limit and a general learning framework

Shi

Yan

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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Background: Optical proximity correction (OPC) is an indispensable technology that has been propelling the advancement of computational lithography technology. To tightly control edge placement error (EPE) and maintain lithography process window, the demands on OPC computational resources and OPC turnaround time are growing rapidly with alarming acceleration. To tame the trend, machine learning technologies have been explored; however, an in-depth discussion on OPC solution learning limit is still lacking.Aim: We aim to present an in-depth discussion on OPC solution learning limit and propose a general machine learning OPC framework that can be extended to curvilinear mask OPC technology.Approach: In this study, we first investigate the machine learning OPC learning limit by examining noninverse lithography technology (non-ILT) OPC solution space characteristics inherited from edge segmentation and control point setting rules and then propose a general machine learning OPC framework that can take full advantage of deep convolution neural network (DCNN) learning capability while being able to preserve mask data high resolution.Results: With this machine learning OPC framework, we have achieved models with average absolute model error <1 nm for 14-nm metal layer. With single GPU, the average time for machine learning OPC models to produce results of 3840 nm × 3840 nm area is 8.74 ms for single channel input model and 12.65 ms for six channels input model. Conclusions:For non-ILT OPC solution, there is an intrinsic learning limit inherited from edge segmentation rules. Machine learning OPC models should be content with learning low order OPC solutions. This intrinsic learning limit of non-ILT OPC solution may diminish for ILT OPC solution when the constraint on degrees of freedom of OPC solution is lifted. The machine learning OPC framework we proposed is general and extendable to curvilinear OPC technology.

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