<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xu, Biying</style></author><author><style face="normal" font="default" size="100%">Lin, Yibo</style></author><author><style face="normal" font="default" size="100%">Xiyuan TANG</style></author><author><style face="normal" font="default" size="100%">Li, Shaolan</style></author><author><style face="normal" font="default" size="100%">Shen, Linxiao</style></author><author><style face="normal" font="default" size="100%">Sun, Nan</style></author><author><style face="normal" font="default" size="100%">Pan, David Z.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">WellGAN: Generative-Adversarial-Network-Guided Well Generation for Analog/Mixed-Signal Circuit Layout</style></title><secondary-title><style face="normal" font="default" size="100%">2019 56th ACM/IEEE Design Automation Conference (DAC)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">1-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>