Full-chip application of machine learning SRAFs on DRAM case using auto pattern selection

Chen, Kun-yuan; Lan, Andy; Yang, Richer; Chen, Vincent; Wang, Shulu; Zhang, Stella; Xu, Xiangru; Yang, Andy; Liu, Sam; Shi, Xiaolong; Li, Angmar; Hsu, Stephen; S, Baron; Zhang, Gary; Gupta, Rachit

doi:10.1117/12.2524051

Cited by 8 publications

(8 citation statements)

References 4 publications

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“…However, today all EDA companies that offer OPC products also offer ILT products of some kind, some used for fixing hotspots like Synopsys, 19,20,98 some for model-based SRAF generation like ASML Brion. [99][100][101] ILT was extended to EUV by Synopsys [102][103][104][105] and ASML Brion started exploring using deep learning (DL) in ILT for SRAF generation. [99][100][101] Despite steady, continuing research and development across academia and industry through the decade and demonstration of the use of ILT to correct full-chip designs, ILT was still seen as an advanced method for use in critical hotspots, rather than as a technique to be applied to full-chip mask generation.…”

Section: History Of Inverse Lithographymentioning

confidence: 99%

“…[99][100][101] ILT was extended to EUV by Synopsys [102][103][104][105] and ASML Brion started exploring using deep learning (DL) in ILT for SRAF generation. [99][100][101] Despite steady, continuing research and development across academia and industry through the decade and demonstration of the use of ILT to correct full-chip designs, ILT was still seen as an advanced method for use in critical hotspots, rather than as a technique to be applied to full-chip mask generation. Excessive computational run-times continued to render full-chip ILT impractical in production settings.…”

Section: History Of Inverse Lithographymentioning

confidence: 99%

“…Inspired by many success stories of ML in a broad range of artificial intelligence applications, both industrial and academic researchers are now actively developing ML solutions for challenging problems in computational lithography, including ILT. [99][100][101]121,[138][139][140] One of the first papers applying DL to ILT is from ASML Brion. Their 2017 paper shows how they use their freeform ILT engine to train an ILT DL model using a convolutional neural network (CNN) (Fig.…”

Section: Applying Deep Learning To Iltmentioning

confidence: 99%

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Inverse lithography technology: 30 years from concept to practical, full-chip reality

Pang

2021

J. Micro/Nanopattern. Mats. Metro.

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In lithography, optical proximity and process bias/effects need to be corrected to achieve the best wafer print. Efforts to correct for these effects started with a simple bias, adding a hammer head in line-ends to prevent line-end shortening. This first-generation correction was called rule-based optical proximity correction (OPC). Then, as chip feature sizes continued to shrink, OPC became more complicated and evolved to a model-based approach. Some extra patterns were added to masks, to improve the wafer process window, a measure of resilience to manufacturing variation. Around this time, the concept of inverse lithography technology (ILT), a mathematically rigorous inverse approach that determines the mask shapes that will produce the desired on-wafer results, was introduced. ILT has been explored and developed over the last three decades as the next generation of OPC, promising a solution to several challenges of advanced-node lithography, whether optical or extreme ultraviolet (EUV). Today, both OPC and ILT are part of an arsenal of lithography technologies called resolution enhancement technologies. Since OPC and ILT both involve computation, they are also considered as part of computational lithography. We explore the background and history of ILT and detail the significant milestones that have taken full-chip ILT from an academic concept to a practical production reality.

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Section: History Of Inverse Lithographymentioning

confidence: 99%

Section: History Of Inverse Lithographymentioning

confidence: 99%

Section: Applying Deep Learning To Iltmentioning

confidence: 99%

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Inverse lithography technology: 30 years from concept to practical, full-chip reality

Pang

2021

J. Micro/Nanopattern. Mats. Metro.

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“…e.g., IPS sensitivity to geometry perturbation [30] (∂I max /∂M, where M is line width). Since the choice of parameter space is only engineering, alternative method is to introduce a machine learning to extract a number of parameters [31] and use them to define a parameter space. Once test patterns are identified in IPS space, any clustering algorithms can be applied for classification purpose: partitioning methods (such as K-means) or hierarchical methods (such as complete-link).…”

Section: B)mentioning

confidence: 99%

Computational Lithography Using Machine Learning Models

Shin

2021

IPSJ Transactions on System LSI Design Methodology

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Machine learning models have been applied to a wide range of computational lithography applications since around 2010. They provide higher modeling capability, so their application allows modeling of higher accuracy. Many applications which are computationally expensive can take advantage of machine learning models, since a well trained model provides a quick estimation of outcome. This tutorial reviews a number of such computational lithography applications that have been using machine learning models. They include mask optimization with OPC (optical proximity correction) and EPC (etch proximity correction), assist features insertion and their printability check, lithography modeling with optical model and resist model, test patterns, and hotspot detection and correction.

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“…Shi et al proposed an optimal feature vector automatic design method based on convolution neural network (CNN), which greatly improved the computational efficiency of ILT [22] . 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%

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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Full-chip application of machine learning SRAFs on DRAM case using auto pattern selection

Cited by 8 publications

References 4 publications

Inverse lithography technology: 30 years from concept to practical, full-chip reality

Inverse lithography technology: 30 years from concept to practical, full-chip reality

Computational Lithography Using Machine Learning Models

Study of Inverse Lithography Approaches based on Deep Learning

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