Accurate etch modeling with massive metrology and deep-learning technology

Lü, Yifei; Zhao, Yuhang; Li, Ming; Yuan, Wei; Peng, Xianghe; Hu, Hongmei; Yao, Shuxin; Liu, Zhunhua; Tian, Yu; Gao, Ying; Pan, Bingyang; Chun-yan, YI; Wang, Jinze; Xie, Qian; Sheng, Xichen; Wu, Ying-chen; Yan, Guanyong; Xiao, Yanjun; Liu, Liang; Liang, Jing; Zhao, Qian; Fan, Yongfa; Zhao, Yiqiong; Mu, Feng; Yao, Yueliang; Yang, Terrance; Lang, Jun

doi:10.1117/12.2552001

Cited by 4 publications

(4 citation statements)

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“…The etch model form used in this work is an Effective Etch Bias (EEB) [6] model from ASML based on contour biasing. This model form provides a much more robust etch OPC solution than an etch bias rule table approach.…”

Section: Model Formsmentioning

confidence: 99%

Etch model calibration and usage in OPC flow for curvilinear layouts

Sungauer

Depre²,

Greca³

2023

DTCO and Computational Patterning II

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The development of optical devices requires the patterning of non-conventional shapes on the wafers [1,2,3,4,5]. In addition to the specific challenges related to the treatment of these curvilinear patterns, an accurate proximity correction must be provided. The Critical Dimensions (CDs) of such patterns are indeed around 100nm, which requires the implementation of advanced lithography processes, similar to CMOS microelectronics technologies. In order to develop a production compliant and robust OPC solution, we previously demonstrated the need for etch bias modelling [1]. Taking the example of optical metasurfaces application, and using ASML’s Tachyon OPC+ platform, we will present the implementation of an OPC flow suitable for curvilinear patterns, starting from the metrology strategy. We will then discuss the etch model calibration methodology, model-based etch bias correction implementation in OPC, and global OPC performance.

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Section: Model Formsmentioning

confidence: 99%

Etch model calibration and usage in OPC flow for curvilinear layouts

Sungauer

Depre²,

Greca³

2023

DTCO and Computational Patterning II

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“…Generative neural networks serving as an inverse design tool for lithography have also been trained in a generative adversarial network framework to produce candidate quasi-optimal masks for given target patterns (Figure b). Concepts from OPC have been readily extended to the modeling of etching errors, where CNNs have been used to accurately model the etching masks for given target patte profiles of nanoscale features (Figure c).…”

Section: Fabrication Of Freeform Devicesmentioning

confidence: 99%

Section: Fabrication Of Freeform Devicesmentioning

confidence: 99%

Algorithm-Driven Paradigms for Freeform Optical Engineering

2022

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Advances in modern manufacturing have enabled the multiscalar patterning of dielectric media with nearly arbitrary layouts, presenting unique opportunities to revolutionize the design and fabrication pipeline for photonic technologies. In this Perspective, we discuss how algorithms based on classical optimization and deep learning are establishing a new conceptual framework for freeform optical engineering. These tools can specify suitable design parameters for a desired objective, automate the high-speed optimization of freeform devices, and augment manufacturing processes to mitigate challenges set by freeform fabrication. A central feature of many of these algorithms is their utilization of data and physics to model and exploit high-dimensional relationships between geometric structure and electromagnetic response within the constraints of Maxwell's equations. We anticipate that these algorithm-driven methods will streamline optical systems design at the physical limits of structured media and become standard academic and industry tools for scientists and engineers.

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“…Further author information: (Send correspondence to Sooyong Lee) *Jeeyong Lee: jeeyong.lee@samsung.com, †Sooyong Lee: sooyong.lee@samsung.com As described above, the classical PPC has revealed a limitation in that it is difficult to properly model the correlation between the design layout and the etch process. Thus, recently there have been attempts to supplement the method through the use of artificial intelligence (AI) technology [6][7][8][9][10]. As rasterized image based AI approaches often cannot ensure their shift invariance, and their results depend on the grid size of their pixel [11,12], they might even worsen the present problem.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to etch-process-aware intensive layout retarget

Lee

Heo

Lee

et al. 2023

Advanced Etch Technology and Process Integration for Nanopatterning XII

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Patterning, a major process in semiconductor manufacturing, aims to transfer the design layout to the wafer. Accordingly, the "process proximity correction" method was developed to overcome the difference in after-cleaninginspected CD (critical dimension) between patterns of similar shapes. However, its physical model is often limited in the predictive performance. Therefore, recent studies have introduced ML (machine learning) technology to supplement model accuracy, but this approach often has an inherent risk of overfitting depending on the type of sampled pattern. In this study, we present a newly invented flow capable of stable etch-process-aware ML modeling by model reconstruction and large amounts of measurement data. The new modeling flow can also be performed within a reasonable runtime through efficient feature extraction. Based on the new model and its related layout targeting platform, intensive improvements were made to CD targeting and spread; for a given layout, in comparison with delicate rule-based modification, the CD targeting accuracy was improved by 4 times and approaches the limit of metrology error.

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Accurate etch modeling with massive metrology and deep-learning technology

Cited by 4 publications

References 0 publications

Etch model calibration and usage in OPC flow for curvilinear layouts

Etch model calibration and usage in OPC flow for curvilinear layouts

Algorithm-Driven Paradigms for Freeform Optical Engineering

A novel approach to etch-process-aware intensive layout retarget

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