2022 ICCAD

Sub-Resolution Assist Feature Generation with Reinforcement Learning and Transfer Learning

Author: Guan-Ting Liu, Wei-Chen Tai, Yi-Ting Lin, Iris Hui-Ru Jiang, James P. Shiely, Pu-Jen Cheng

Affiliation: Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan

Abstract:

As modern photolithography feature sizes continue to shrink, sub-resolution assist feature (SRAF) generation has become a key resolution enhancement technique to improve the manufacturing process window. State-of-the-art works resort to machine learning to overcome the deficiencies of model-based and rule-based approaches. Nevertheless, these machine learning-based methods do not consider or implicitly consider the optical interference between SRAFs, and highly rely on post-processing to satisfy SRAF mask manufacturing rules. In this paper, we are the first to generate SRAFs using reinforcement learning to address SRAF interference and produce mask-rule-compliant results directly. In this way, our two-phase learning enables us to emulate the style of model-based SRAFs while further improving the process variation (PV) band. A state alignment and action transformation mechanism is proposed to achieve orientation equivariance while expediting the training process. We also propose a transfer learning framework, allowing SRAF generation under different light sources without retraining the model. Compared with state-of-the-art works, our method improves the solution quality in terms of PV band and edge placement error (EPE) while reducing the overall runtime.

2021 TCAD

GAN-SRAF: Subresolution Assist Feature Generation Using Generative Adversarial Networks

Author: Mohamed Baker Alawieh, Yibo Lin, Zaiwei Zhang, Meng Li, Qixing Huang, and David Z Pan.

Affiliation: University of Texas at Austin: Austin, TX, US

Abstract:

As the integrated circuits (ICs) technology continues to scale, resolution enhancement techniques (RETs) are mandatory to obtain high manufacturing quality and yield. Among various RETs, subresolution assist feature (SRAF) generation is a key technique to improve the target pattern quality and lithographic process window. While mode-based SRAF insertion techniques have demonstrated high accuracy, they usually suffer from high computational cost. Therefore, more efficient techniques that can achieve high accuracy while reducing runtime are in strong demand. In this article, we leverage the recent advancement in machine learning for image generation to tackle the SRAF insertion problem. In particular, we propose a new SRAF insertion framework, GAN-SRAF, which uses generative adversarial networks (GANs) to generate SRAFs directly for any given layout. Our proposed approach incorporates a novel layout to image encoding using multichannel heatmaps to preserve the layout information and facilitate layout reconstruction. Our experimental results demonstrate ∼14.6× reduction in runtime when compared to the previous best machine learning approach for SRAF generation, and ∼144× reduction compared to the mode-based approach, while achieving comparable quality of results.

2019 ASP-DAC

SRAF Insertion via Supervised Dictionary Learning

Author: Hao Geng, Haoyu Yang, Yuzhe Ma, Joydeep Mitra, and Bei Yu.

Affiliation: Chinese University of Hong Kong, Hong Kong, Dalian University of Technology, Dalian, China, Chinese University of Hong Kong, Hong Kong, PCB Team, Cadence Design Systems, San Jose, USA

Abstract:

In modern VLSI design flow, sub-resolution assist feature (SRAF) insertion is one of the resolution enhancement techniques (RETs) to improve chip manufacturing yield. With aggressive feature size continuously scaling down, layout feature learning becomes extremely critical. In this paper, for the first time, we enhance conventional manual feature construction, by proposing a supervised online dictionary learning algorithm for simultaneous feature extraction and dimensionality reduction. By taking advantage of label information, the proposed dictionary learning engine can discriminatively and accurately represent the input data. We further consider SRAF design rules in a global view, and design an integer linear programming model in the post-processing stage of SRAF insertion framework. Experimental results demonstrate that, compared with a state-of-the-art SRAF insertion tool, our framework not only boosts the mask optimization quality in terms of edge placement error (EPE) and process variation (PV) band area, but also achieves some speed-up.

2016 ISPD

A Machine Learning Based Framework for Sub-Resolution Assist Feature Generation

Author: Xiaoqing Xu, Tetsuaki Matsunawa, Shigeki Nojima, Chikaaki Kodama, Toshiya Kotani, and David Z. Pan.

Affiliation: University of Texas at Austin, Austin, TX, USA

Abstract:

Sub-Resolution Assist Feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. model- based SRAF generation has been widely used to achieve high accuracy but it is known to be time consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. This paper proposes the first ma- chine learning based framework for fast yet consistent SRAF generation with high quality of results. Our technical con- tributions include robust feature extraction, novel feature compaction, model training for SRAF classification and pre- diction, and the final SRAF generation with consideration of practical mask manufacturing constraints. Experimental re- sults demonstrate that, compared with commercial Calibre tool, our machine learning based SRAF generation obtains 10X speed up and comparable performance in terms of edge placement error (EPE) and process variation (PV) band.

AI+EDA

Sub-resolution assist feature generation