DREAMPlace 3.0

Gu, Jiaqi; Jiang, Zixuan; Lin, Yibo; Pan, David Z.

doi:10.1145/3400302.3415691

Cited by 16 publications

(2 citation statements)

References 34 publications

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“…In table 3, we compare GoPlace to prior state-of-art methods GraphPlace [1], DeepPR [6] and Maskplace [7] on the full placement task. RL-based methods are used for macro placement and the well-tested placer DREAMPlace [9] is used for the remaining standard cells placement. It can be observed that GoPlace also achieves the best full placement performance.…”

Section: More Resultsmentioning

confidence: 99%

GoPlace: chip placement like playing go

Hu,

Shen,

Ding

et al. 2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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Section: More Resultsmentioning

confidence: 99%

GoPlace: chip placement like playing go

Hu,

Shen,

Ding

et al. 2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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“…15 These programs can even adjust sections of their own code based on gradient information. This coding philosophy has proven its worth in various research areas, [16][17][18][19][20] especially in imaging, showcasing its potential in resolving challenges faced by current computational lithography techniques. 21 As depicted in Figure 2, two modes exist for performing automatic differentiation: forward and backward (or reverse).…”

Section: Index Of Refractionmentioning

confidence: 99%

Open-source differentiable lithography imaging framework

Chen,

Geng,

et al. 2024

DTCO and Computational Patterning III

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The rapid evolution of the electronics industry, driven by Moore's law and the proliferation of integrated circuits, has led to significant advancements in modern society, including the Internet, wireless communication, and artificial intelligence (AI). Central to this progress is optical lithography, a critical technology in semiconductor manufacturing that accounts for approximately 30% to 40% of production costs. As semiconductor nodes shrink and transistor numbers increase, optical lithography becomes increasingly vital in current integrated circuit (IC) fabrication technology. This paper introduces an open-source differentiable lithography imaging framework that leverages the principles of differentiable programming and the computational power of GPUs to enhance the precision of lithography modeling and simplify the optimization of resolution enhancement techniques (RETs). The framework models the core components of lithography as differentiable segments, allowing for the implementation of standard scalar imaging models, including the Abbe and Hopkins models, as well as their approximation models. The paper introduces a computational lithography framework that optimizes semiconductor manufacturing processes using advanced computational techniques and differentiable programming. It compares imaging models and provides tools for enhancing resolution, demonstrating improved semiconductor patterning performance. The open-sourced framework represents a significant advancement in lithography technology, facilitating collaboration in the field. The source code is available at https://github.com/TorchOPC/TorchLitho.

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