Cited By
View all- Zheng SMa YYu BWong MDe V(2024)EMOGen: Enhancing Mask Optimization via Pattern GenerationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655680(1-6)Online publication date: 23-Jun-2024
Data-Driven Approaches for Process Simulation and Optical Proximity Correction
Pages 721 - 726
Abstract
With continuous shrinking of process nodes, semiconductor manufacturing encounters more and more serious inconsistency between designed layout patterns and resulted wafer images. Conventionally, examining how a layout pattern can deviate from its original after complicated process steps, such as optical lithography and subsequent etching, relies on computationally expensive process simulation, which suffers from incredibly long runtime for large-scale circuit layouts, especially in advanced nodes. In addition, being one of the most important and commonly adopted resolution enhancement techniques, optical proximity correction (OPC) corrects image errors due to process effects by moving segment edges or adding extra polygons to mask patterns, while it is generally driven by simulation or time-consuming inverse lithography techniques (ILTs) to achieve acceptable accuracy. As a result, more and more state-of-the-art works on process simulation or/and OPC resort to the fast inference characteristic of machine/deep learning. This paper reviews these data-driven approaches to highlight the challenges in various aspects, explore preliminary solutions, and reveal possible future directions to push forward the frontiers of the research in design for manufacturability.
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Index Terms
- Data-Driven Approaches for Process Simulation and Optical Proximity Correction
Index terms have been assigned to the content through auto-classification.
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Published: 31 January 2023
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View all- Zheng SMa YYu BWong MDe V(2024)EMOGen: Enhancing Mask Optimization via Pattern GenerationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655680(1-6)Online publication date: 23-Jun-2024
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