Machine Learning using retarget data to improve accuracy of fast lithographic hotspot detection

Aliaa Kabeel; Wael ElManhawy; Joe Kwan; Asmaa Rabie; Mohamed Ismail; Ahmed Khater; Sarah Rizk

doi:10.1117/12.2551827

23 March 2020 Machine Learning using retarget data to improve accuracy of fast lithographic hotspot detection

Aliaa Kabeel, Wael ElManhawy, Joe Kwan, Asmaa Rabie, Mohamed Ismail, Ahmed Khater, Sarah Rizk

Proceedings Volume 11328, Design-Process-Technology Co-optimization for Manufacturability XIV; 1132803 (2020) https://doi.org/10.1117/12.2551827
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

As the litho hotspot detection runtime is currently in a continuous increase with sub-10nm technology nodes due to the increase of the design and process complexity, new methods and approaches are needed to improve the runtime while guaranteeing high accuracy rate. Machine-Learning Fast LFD (ML-FLFD) is a new flow that uses a specialized machine learning technique to provide fast and accurate litho hotspot detection. This methodology is based on having input data to train the machine learning model during the model preparation phase. Current ML-FLFD techniques depend on collecting hotspots (HS) and Non hotspots (NHS) data from the drawn layer in order to train the model. In this paper, we present a new technique where we use the retarget data to train the machine learning model instead of using the drawn hotspot data. Using retargeting data is getting one step closer to the actual printed contours which gives a better insight about the hotspots of the manufactured wires during the machine learning model training step. The effect of using closer data to the printed contours will be reflected on both the accuracy and the extra rate which will reduce simulation area. In the different sections of this paper, we will compare the new approach of using retarget data as a ML input to the current technique of using drawn data. Pros and cons of the two approaches will be listed in details including the experimental results of hotspot accuracy and litho simulation area.

Conference Presentation

Citation Download Citation

Aliaa Kabeel, Wael ElManhawy, Joe Kwan, Asmaa Rabie, Mohamed Ismail, Ahmed Khater, and Sarah Rizk "Machine Learning using retarget data to improve accuracy of fast lithographic hotspot detection", Proc. SPIE 11328, Design-Process-Technology Co-optimization for Manufacturability XIV, 1132803 (23 March 2020); https://doi.org/10.1117/12.2551827

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