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Machine learning guided curvilinear MPC
Author(s): Malavika Sharma; Bhardwaj Durvasula; Nageswara Rao; Ingo Bork; Rachit Sharma; Kushlendra Mishra; Peter Buck
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Paper Abstract

With the advancement of semiconductor technology beyond 7nm, the speed and accuracy constraints on computational lithography are tightening. As the mask features become smaller and more complex, Inverse Lithography Technology (ILT) is increasingly being considered as a possible OPC solution in order to maximize process win- dow (PW) and improve CD uniformity (CDU). Until recently there has been a limitation on the adoption of curvilinear masks due to their undesirably long mask write times using vector shaped beam (VSB) mask writers, but with the introduction of Multi-beam mask writers (MBMW) in volume photomask production, mask write time is no longer a limiting factor for the usage of curvilinear masks. The key differences between correcting ILT patterns as compared to correcting rectilinear patterns explain the complexity associated with Curvilinear MPC and the corresponding longer convergence time. Continuous efforts have been made by the computational lithography community to employ solutions from the ever evolving machine learning technology. Machine learning based solutions have been proposed for a variety of problems like mask making proximity effect correction, model based OPC, ILT and hot spot detection. An artificial neural network is an information processing system inspired by the biological nervous system in the way the brain processes information. It consists of large number of highly interconnected processing elements (neurons), working together to solve specific problems. It is a powerful data modelling tool that captures complex input/output relationships. In this work we present a neural network based solution which predicts a smart pre-bias for curvilinear features, leading to faster convergence of the correction engine.

Paper Details

Date Published: 3 October 2019
PDF: 7 pages
Proc. SPIE 11148, Photomask Technology 2019, 111480Q (3 October 2019); doi: 10.1117/12.2538646
Show Author Affiliations
Malavika Sharma, Mentor Graphics (India) Private Ltd. (India)
Bhardwaj Durvasula, Mentor Graphics (India) Private Ltd. (India)
Nageswara Rao, Mentor Graphics (India) Private Ltd. (India)
Ingo Bork, Mentor, a Siemens Business (United States)
Rachit Sharma, Mentor Graphics (India) Private Ltd. (India)
Kushlendra Mishra, Mentor Graphics (India) Private Ltd. (India)
Peter Buck, Mentor, a Siemens Business (United States)


Published in SPIE Proceedings Vol. 11148:
Photomask Technology 2019
Jed H. Rankin; Moshe E. Preil, Editor(s)

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