Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction using Graph Neural Network and U-Net

An accurate DRC (design rule check) hotspot prediction at the placement stage is essential in order to reduce a substantial amount of design time required for the iterations of placement and routing. It is known that for implementing chips with advanced technology nodes, (1) pin accessibility and (2...

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Bibliographic Details
Published in:2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD) pp. 1 - 9
Main Authors: Baek, Kyeonghyeon, Park, Hyunbum, Kim, Suwan, Choi, Kyumyung, Kim, Taewhan
Format: Conference Proceeding
Language:English
Published: ACM 29.10.2022
Subjects:
Benchmark testing
Data aggregation
Feature extraction
Graph neural networks
Machine learning
Predictive models
Routing
ISSN:1558-2434
Online Access:Get full text
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Summary:An accurate DRC (design rule check) hotspot prediction at the placement stage is essential in order to reduce a substantial amount of design time required for the iterations of placement and routing. It is known that for implementing chips with advanced technology nodes, (1) pin accessibility and (2) routing congestion are two major causes of DRVs (design rule violations). Though many ML (machine learning) techniques have been proposed to address this prediction problem, it was not easy to assemble the aggregate data on items 1 and 2 in a unified fashion for training ML models, resulting in a considerable accuracy loss in DRC hotspot prediction. This work overcomes this limitation by proposing a novel ML based DRC hotspot prediction technique, which is able to accurately capture the combined impact of items 1 and 2 on DRC hotspots. Precisely, we devise a graph, called pin proximity graph, that effectively models the spatial information on cell I/O pins and the information on pin-to-pin disturbance relation. Then, we propose a new ML model, called PGNN, which tightly combines GNN (graph neural network) and U-net in a way that GNN is used to embed pin accessibility information abstracted from our pin proximity graph while U-net is used to extract routing congestion information from grid-based features. Through experiments with a set of benchmark designs using Nangate 15nm library, our PGNN outperforms the existing ML models on all benchmark designs, achieving on average 7.8~12.5% improvements on F1-score while taking 5.5× fast inference time in comparison with that of the state-of-the-art techniques.
ISSN:1558-2434
DOI:10.1145/3508352.3549346