WellGAN

Xu, Biying; Lin, Yibo; Tang, Xiyuan; Li, Shaolan; Shen, Linxiao; Sun, Nan; Pan, David Z.

doi:10.1145/3316781.3317930

Cited by 43 publications

(2 citation statements)

References 14 publications

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“…The second direction involves extracting rules from manual layouts. WellGAN [ 10 ] utilizes GANs (generative adversarial networks) to predict the placement of manually well-designed structures, aiming to achieve better performance metrics (area and wirelength) while ensuring compliance with design rules for various processes. GeniusRoute [ 11 ], on the other hand, employs a generative encoder model to learn the rules of manual analog routing.…”

Section: Introductionmentioning

confidence: 99%

Parsing Netlists of Integrated Circuits from Images via Graph Attention Network

Hu,

Zhan,

Tong

2023

Sensors

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A massive number of paper documents that include important information such as circuit schematics can be converted into digital documents by optical sensors like scanners or digital cameras. However, extracting the netlists of analog circuits from digital documents is an exceptionally challenging task. This process aids enterprises in digitizing paper-based circuit diagrams, enabling the reuse of analog circuit designs and the automatic generation of datasets required for intelligent design models in this domain. This paper introduces a bottom-up graph encoding model aimed at automatically parsing the circuit topology of analog integrated circuits from images. The model comprises an improved electronic component detection network based on the Swin Transformer, an algorithm for component port localization, and a graph encoding model. The objective of the detection network is to accurately identify component positions and types, followed by automatic dataset generation through port localization, and finally, utilizing the graph encoding model to predict potential connections between circuit components. To validate the model’s performance, we annotated an electronic component detection dataset and a circuit diagram dataset, comprising 1200 and 3552 training samples, respectively. Detailed experimentation results demonstrate the superiority of our proposed enhanced algorithm over comparative algorithms across custom and public datasets. Furthermore, our proposed port localization algorithm significantly accelerates the annotation speed of circuit diagram datasets.

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Section: Introductionmentioning

confidence: 99%

Parsing Netlists of Integrated Circuits from Images via Graph Attention Network

Hu,

Zhan,

Tong

2023

Sensors

View full text Add to dashboard Cite

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“…Recently, generative adversarial networks (GAN) [16] gained much traction as it can learn features (latent representation) without extensively annotated training data. GAN-based methods have been applied for VLSI physical designs such as generation of various noise maps to facilitate the IR-drop noise sensor placement [22], for layout lithography analysis [30] and sub-resolution assist feature generation [4], for analog layout well generation [29]. However, the proposed GAN-based design and analysis techniques are mainly targeted for the statistical and static image generations (analysis).…”

Section: Introductionmentioning

confidence: 99%