1. [Publications](/publications) 2. TAG: Learning Circuit Spatial Embedding from Layouts # TAG: Learning Circuit Spatial Embedding from Layouts ![](/sites/default/files/styles/wide/public/publications/TAG.JPG?itok=_2jWtPOD) Analog and mixed-signal (AMS) circuit designs still rely on human design expertise. Machine learning has been assisting circuit design automation by replacing human experience with artificial intelligence. This paper presents TAG, a new paradigm of learning the circuit representation from layouts leveraging **T**ext, self **A**ttention and **G**raph. The embedding network model learns spatial information without manual labeling. We introduce text embedding and a self-attention mechanism to AMS circuit learning. Experimental results demonstrate the ability to predict layout distances between instances with industrial FinFET technology benchmarks. The effectiveness of the circuit representation is verified by showing the transferability to three other learning tasks with limited data in the case studies: layout matching prediction, wirelength estimation, and net parasitic capacitance prediction. ## Authors Keren Zhu (University of Texas at Austin) Hao Chen (University of Texas at Austin) [Walker Turner](/person/walker-turner) George F. Kokai (NVIDIA) Po-Hsuan Wei (NVIDIA) David Z. Pan (University of Texas at Austin) Mark Haoxing Ren (NVIDIA) ## Publication Date Sunday, October 30, 2022 ## Published in [2022 International Conference on Computer-Aided Design](https://iccad.com) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Circuits and VLSI Design](/research-area/circuits) ## External Links [Paper](https://dl.acm.org/doi/10.1145/3508352.3549384) ## Copyright Copyright by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or . The definitive version of this paper can be found at ACM's Digital Library .