Machine learning-based 3D resist model

Shim, Seongbo; Choi, Suhyeong; Shin, Youngsoo

doi:10.1117/12.2257904

Cited by 14 publications

(13 citation statements)

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“…A simple MLP network with local layout densities as shown in Fig. 7 and optical kernel signals as inputs produces accurate prediction of resist height [23], or a similar network may be trained to predict whether resist will remain after etch process.…”

Section: Estimation Of 3d Resist Profilementioning

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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Section: Estimation Of 3d Resist Profilementioning

confidence: 99%

Computational Lithography Using Machine Learning Models

Shin

2021

IPSJ Transactions on System LSI Design Methodology

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Section: Introductionmentioning

confidence: 99%

“…Accurate lithography simulation like rigorous physics-based simulation is notorious for its long computational time, while simulation with compact models suffers from accuracy issues [3], [4]. On the other hand, machine learning techniques are able to construct accurate models and then make efficient predictions.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches first take training data to calibrate a model and then use this model to make predictions on testing data for validation. The effectiveness of learning-based solutions has been studied in various lithography related areas including aerial image simulation [5], hotspot detection [6]- [11], optical proximity correction (OPC) [12]- [15], subresolution assist features (SRAF) [16], [17], resist modeling [3], [4], etc. In resist modeling, a convolutional neural network (CNN) that predicts slicing thresholds in aerial images is proposed [4].…”

Section: Introductionmentioning

confidence: 99%

“…Around 70% improvement in accuracy is reported compared with calibrated compact models from Mentor Calibre [18]. Shim et al [3] propose an artificial neural network (ANN) with five hidden layers to predict the height of resist after exposure. Significant speedup is reported with high accuracy compared with a rigorous simulation.…”

Section: Introductionmentioning

confidence: 99%

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Data Efficient Lithography Modeling With Transfer Learning and Active Data Selection

Lin

Watanabe

et al. 2019

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Lithography simulation is one of the key steps in physical verification, enabled by the substantial optical and resist models. A resist model bridges the aerial image simulation to printed patterns. While the effectiveness of learning-based solutions for resist modeling has been demonstrated, they are considerably data-demanding. Meanwhile, a set of manufactured data for a specific lithography configuration is only valid for the training of one single model, indicating low data efficiency. Due to the complexity of the manufacturing process, obtaining enough data for acceptable accuracy becomes very expensive in terms of both time and cost, especially during the evolution of technology generations when the design space is intensively explored. In this work, we propose a new resist modeling framework for contact layers, utilizing existing data from old technology nodes and active selection of data in a target technology node, to reduce the amount of data required from the target lithography configuration. Our framework based on transfer learning and active learning techniques is effective within a competitive range of accuracy, i.e., 3-10X reduction on the amount of training data with comparable accuracy to the state-of-the-art learning approach.

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Machine Learning for Compact Lithographic Process Models

Shiely

2019

Machine Learning in VLSI Computer-Aided Design

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Machine learning-based 3D resist model

Cited by 14 publications

References 1 publication

Computational Lithography Using Machine Learning Models

Computational Lithography Using Machine Learning Models

Data Efficient Lithography Modeling With Transfer Learning and Active Data Selection

Machine Learning for Compact Lithographic Process Models

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