Machine learning guided curvilinear MPC

Malavika Sharma; Bhardwaj Durvasula; Nageswara Rao; Ingo Bork; Rachit Sharma; Kushlendra Mishra; Peter Buck

doi:10.1117/12.2538646

3 October 2019 Machine learning guided curvilinear MPC

Malavika Sharma, Bhardwaj Durvasula, Nageswara Rao, Ingo Bork, Rachit Sharma, Kushlendra Mishra, Peter Buck

Proceedings Volume 11148, Photomask Technology 2019; 111480Q (2019) https://doi.org/10.1117/12.2538646
Event: SPIE Photomask Technology + EUV Lithography, 2019, Monterey, California, United States

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.

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Malavika Sharma, Bhardwaj Durvasula, Nageswara Rao, Ingo Bork, Rachit Sharma, Kushlendra Mishra, and Peter Buck "Machine learning guided curvilinear MPC", Proc. SPIE 11148, Photomask Technology 2019, 111480Q (3 October 2019); https://doi.org/10.1117/12.2538646

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KEYWORDS

Photomasks

Machine learning

Optical proximity correction

Computational lithography

Data modeling

Lithography

Neural networks

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