@conference {8806799,
	title = {WellGAN: Generative-Adversarial-Network-Guided Well Generation for Analog/Mixed-Signal Circuit Layout},
	booktitle = {2019 56th ACM/IEEE Design Automation Conference (DAC)},
	year = {2019},
	month = {June},
	pages = {1-6},
	abstract = {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.},
	issn = {0738-100X},
	author = {Xu, Biying and Lin, Yibo and Xiyuan TANG and Li, Shaolan and Shen, Linxiao and Sun, Nan and Pan, David Z.}
}
