Machine learning optical proximity correction with generative adversarial networks

Abstract

Background: Algorithmic breakthroughs in machine learning (ML) have allowed increasingly more applications developed for computational lithography, gradually shifting focus from hotspot detection to inverse lithography and optical proximity correction (OPC). We proposed a pixelated mask synthesis method utilizing deep-learning techniques, to generate afterdevelopment-inspection (ADI) contour and mask feature generation.Aim: Conventional OPC correction consists of two parts, the simulation model which predicts the expected contour signal, and the correction script that modifies the actual layout. With practicality in mind, we collected modeling wafer data from scratch, then implemented ML models to reproduce conventional OPC actions, mask to contour prediction, and design to mask correction.Approach: Two generative adversarial networks (GANs) were constructed, a pix2pix model was first trained to learn the correspondences between mask image and paired ADI contour image collected on wafer. The second model is embedded into machine learning mask correction (ML-OPC) framework, output mask is optimized through minimizing pixel difference between design target and simulated contour.Results: Two different magnification SEM image datasets were collected and studied, with the higher magnification showing better simulator pixel accuracy. Supervised training of the correction model provided a quick prototype mask synthesis generator, and combination of unsupervised training allowed mask pattern synthetization from any given design layout. Conclusions:The experimental results demonstrated that our ML-OPC framework was able to mimic conventional OPC model in producing exquisite mask patterns and contours. This ML-OPC framework could be implemented across full chip layout.

Geometrical positioning surveying-based features for BEOL line-end-pull-back modeling using regression-based machine-learning

“…In the conventional approach, LEPB modeling is conducted as a part of OPC modeling 32 and is objective by an edge-placement-error convergence at LEs, which mandates careful tuning for LE fragments in the OPC recipes. ML-OPC 20 – 22 , 33 and GPU-based OPC 34 have been proposed to accelerate OPC modeling. LEPB amounts can be precisely characterized through calibrated model-based litho (optical and etching) simulations, which require high computational resources consumption.…”