Deep learning–based inverse method for layout design

Zhang, Yujie; Ye, Wenjing

doi:10.1007/s00158-019-02222-w

Cited by 39 publications

(18 citation statements)

References 57 publications

(61 reference statements)

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“…The model proposed in [15] synthesizes designs with hierarchical structures in two stages: the first synthesizes the parent shape from a learned latent representation; and the second synthesizes a child shape from another learned latent representation conditioned on the parent shape. b) Synthesis of layouts with constraints -The production of layout designs with complex constraints is a problem of searching for an optimal solution in a design space confined by constraints [16]. It is a particularly challenging problem due to the non-uniqueness of the solution and also because it is difficult to identify a set of suitable design variables that define the layout.…”

Section: ) Synthesis Of 3d Designsmentioning

confidence: 99%

“…It is a particularly challenging problem due to the non-uniqueness of the solution and also because it is difficult to identify a set of suitable design variables that define the layout. Also, it is challenging to incorporate the constraints into conventional optimization-based methods [16].…”

Section: ) Synthesis Of 3d Designsmentioning

confidence: 99%

“…Reference [16] proposes a method that uses VAE to learn the constraints and to generate layout design candidates that automatically satisfy all the constraints. The method is based on the assumption that a design space is a specific space associated with a certain but unknown distribution [16]. A VAE model can be constructed to learn the unknown distribution of a design space by providing a dataset that satisfies all the constraints.…”

Section: ) Synthesis Of 3d Designsmentioning

confidence: 99%

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Boosting Business With Machine Learning-Based Automated Visual Data Processing: Results of Finnish Company Interviews

et al. 2020

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Machine Learning (ML) solutions are rapidly evolving and are increasingly capable of performing Automated Visual Data Processing (AVDP) tasks such as visual scene understanding, 3D model reconstruction and automated content generation tasks. We believe that novel AVDP solutions can significantly boost a company's business, streamline the creation of 3D content and models, enable adaptation of content in XR applications and support the creation of digital twins for a company's needs. However, there are also obstacles that limit the business use of such solutions. The objective of our research is to study the skills and insights of companies in using different kinds of AVDP solutions in their business. This article presents the results of interviews with 10 Finnish companies. The interviews comprised three sections: The first section gave a brief introduction about the existing AVDP solutions for the respondents. The second section collected information on the respondents' background and the respective company's experience of using ML in business and its short and long-term interest in developing or using different AVDP solutions in its business. The third section comprised thematic interviews which covered visual data processing themes that the respondents selected to be of interest to their company's business. INDEX TERMS Machine learning, business, visual scene understanding, 3D model reconstruction, automated content generation.

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Section: ) Synthesis Of 3d Designsmentioning

confidence: 99%

Section: ) Synthesis Of 3d Designsmentioning

confidence: 99%

Section: ) Synthesis Of 3d Designsmentioning

confidence: 99%

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Boosting Business With Machine Learning-Based Automated Visual Data Processing: Results of Finnish Company Interviews

et al. 2020

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“…Chen et al used Auto Pattern Selection (APS) tool to train the Newron SRAF deep learning network and successfully realized the inverse mask optimization on full-chip layout [23] . As examples, this section will detail two ILT methods based on variational autoencoder (VAE) [24] and generative adversarial network (GAN) [25] .…”

Section: Ilt Based On Standard Deep Learningmentioning

confidence: 99%

“…The (a) architecture of VAE, (b) control points, and (c) the optimized mask and print image obtained by VAE method (revised from Figs. 1, 7 and 10 in Ref [24]…”

mentioning

confidence: 95%

Study of Inverse Lithography Approaches based on Deep Learning

Zhang¹,

Ma²,

Zhang³

et al. 2019

Journal of Microelectronic Manufacturing

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Computational lithography (CL) has become an indispensable technology to improve imaging resolution and fidelity of deep sub-wavelength lithography. The state-of-the-art CL approaches are capable of optimizing pixel-based mask patterns to effectively improve the degrees of optimization freedom. However, as the growth of data volume of photomask layouts, computational complexity has become a challenging problem that prohibits the applications of advanced CL algorithms. In the past, a number of innovative methods have been developed to improve the computational efficiency of CL algorithms, such as machine learning and deep learning methods. Based on the brief introduction of optical lithography, this paper reviews some recent advances of fast CL approaches based on deep learning. At the end, this paper briefly discusses some potential developments in future work.

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Frequency‐Coded Passive Multifunctional Elastic Metasurfaces

Rong

Zhang

et al. 2020

Adv Funct Materials

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As an emerging material, metasurfaces open the door to a wide range of applications based on ultrasonic and elastic waves. However, most of the existing elastic metasurfaces have a fixed functionality and operate at a fixed frequency, which make them difficult to adapt to changing working requirements and/or environments. Although several recently proposed reconfigurable designs can relax this limitation, they either need laborious tuning or complex active control systems. In this work, by encoding multiple functionalities onto a single metasurface through operation frequencies, passive metasurfaces with switchable multifunctionalities are proposed and realized. Different wave manipulation functions of a single metasurface can be switched from one to another simply by changing the operation frequency. The design of these metasurfaces is realized using a systematic design approach based on topology optimization. Frequency-coded multiple elastic wave manipulation functions, including wave beam steering and focusing, are numerically and experimentally demonstrated. Good agreement between the numerical simulations and experimental measurements is achieved. The proposed design strategy significantly enhances the functionalities and adaptivity of metasurfaces, moving them closer towards real-world applications.

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Deep learning–based inverse method for layout design

Cited by 39 publications

References 57 publications

Boosting Business With Machine Learning-Based Automated Visual Data Processing: Results of Finnish Company Interviews

Boosting Business With Machine Learning-Based Automated Visual Data Processing: Results of Finnish Company Interviews

Study of Inverse Lithography Approaches based on Deep Learning

Frequency‐Coded Passive Multifunctional Elastic Metasurfaces

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