WellGAN: Generative-Adversarial-Network-Guided Well Generation for Analog/Mixed-Signal Circuit Layout
WellGAN: Generative-Adversarial-Network-Guided Well Generation for Analog/Mixed-Signal Circuit Layout
复制标题
DOI：
10.1145/3316781.3317930
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Biying Xu;Yibo Lin;Xiyuan Tang;Shaolan Li;Linxiao Shen;Nan Sun;D. Pan
中科院分区：
文献类型：
--
作者：
Biying Xu;Yibo Lin;Xiyuan Tang;Shaolan Li;Linxiao Shen;Nan Sun;D. Pan
在后端模拟/混合信号（AMS）设计流程中，阱生成一直是布局紧凑性、布线复杂性、电路性能和鲁棒性的基本挑战。AMS布局自动化工具的不成熟在很大程度上来自于难以理解和整合设计师的专业知识。为了模仿经验丰富的设计师在井生成中的行为，我们提出了一个生成对抗网络（GAN）引导的井生成框架，该框架具有利用先前高质量手动制作的布局的后细化阶段。该方法首先通过训练好的GAN模型生成威尔斯井的引导区域，然后通过后期精化使生成的井结果合法化，以满足设计规则。实验结果表明，所提出的技术是能够产生威尔斯接近手工设计具有可比的布局后的电路性能。
In back-end analog/mixed-signal (AMS) design flow, well generation persists as a fundamental challenge for layout compactness, routing complexity, circuit performance and robustness. The immaturity of AMS layout automation tools comes to a large extent from the difficulty in comprehending and incorporating designer expertise. To mimic the behavior of experienced designers in well generation, we propose a generative adversarial network (GAN) guided well generation framework with a post-refinement stage leveraging the previous high-quality manually-crafted layouts. Guiding regions for wells are first created by a trained GAN model, after which the well generation results are legalized through post-refinement to satisfy design rules. Experimental results show that the proposed technique is able to generate wells close to manual designs with comparable post-layout circuit performance.