Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning

TL;DR

This paper introduces a deep learning approach combining global-local novelty detection and active learning to efficiently identify and update models with unseen layout patterns in lithography simulation, reducing the need for extensive data collection.

Contribution

It presents a novel global-local novelty detection framework and active-learning strategies to improve model updating with minimal data, addressing the challenge of unseen layout patterns in lithography simulation.

Findings

Effective detection of novel layout patterns without ground-truth shapes

Active learning strategies select representative layouts for model updating

Improved model accuracy with fewer training samples

Abstract

Learning-based pre-simulation (i.e., layout-to-fabrication) models have been proposed to predict the fabrication-induced shape deformation from an IC layout to its fabricated circuit. Such models are usually driven by pairwise learning, involving a training set of layout patterns and their reference shape images after fabrication. However, it is expensive and time-consuming to collect the reference shape images of all layout clips for model training and updating. To address the problem, we propose a deep learning-based layout novelty detection scheme to identify novel (unseen) layout patterns, which cannot be well predicted by a pre-trained pre-simulation model. We devise a global-local novelty scoring mechanism to assess the potential novelty of a layout by exploiting two subnetworks: an autoencoder and a pretrained pre-simulation model. The former characterizes the global structural dissimilarity between a given layout and training samples, whereas the latter extracts a latent code representing the fabrication-induced local deformation. By integrating the global dissimilarity with the local deformation boosted by a self-attention mechanism, our model can accurately detect novelties without the ground-truth circuit shapes of test samples. Based on the detected novelties, we further propose two active-learning strategies to sample a reduced amount of representative layouts most worthy to be fabricated for acquiring their ground-truth circuit shapes. Experimental results demonstrate i) our method's effectiveness in layout novelty detection, and ii) our active-learning strategies' ability in selecting representative novel layouts for keeping a learning-based pre-simulation model updated.