Neural Network Classifier-Based OPC With Imbalanced Training Data

Choi, Suhyeong; Shim, Seongbo; Shin, Youngsoo

doi:10.1109/tcad.2018.2824255

Cited by 15 publications

(8 citation statements)

References 26 publications

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“…Further reduction in code execution time can be done by reducing the output neuron number. This can be done by using feature extraction to highlight the key geometry parameters, as the case in [23]. Alternatively, we can reduce the output neuron number by evaluating the averaged difference between the real images and predicted images at predefined partitions over the entire output images.…”

Section: Resultsmentioning

confidence: 99%

Intelligent Photolithography Corrections Using Dimensionality Reductions

et al. 2019

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With the shrinking of the IC technology node, optical proximity effects (OPC) and etch proximity effects (EPC) are the two major tasks in advanced photolithography patterning. Machine learning has emerged in OPC/EPC problems because conventional optical-solver-based OPC is time-consuming, and there is no physical model existing for EPC. In this work, we use dimensionality reduction (DR) algorithms to reduce the computation time of complex OPC/EPC problems while the prediction accuracy is maintained. Also, we implement a pure machine learning approach where the input masks are directly mapped to the output etched patterns. While one photolithographic mask can generate many experimental patterns at once, our pure ML-based approach can shorten the trial-and-error period in the photolithographic correction. Additionally, we demonstrate the automation in SEM images preprocessing using feature detection, and this facilitates intelligent manufacturing in semiconductor processing. The input vector dimensions are effectively reduced by two orders of magnitude while the observed mean squared error is not affected significantly. The computation runtime is reduced from 4804 s of the baseline calculation to 10 s-200 s The MSE values changed from the baseline 0.037 to 0.037 for singular value decomposition (SVD), to 0.039 for independent component analysis (ICA), and to 0.035 for factor analysis (FA).

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Section: Resultsmentioning

confidence: 99%

Intelligent Photolithography Corrections Using Dimensionality Reductions

et al. 2019

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“…To alleviate the long simulation runtime, numerous machine learning-based OPC models (MLOPC) have been proposed [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. Works from R. Frye [28] and P. Jedrasik [29] have implemented unsupervised neural networks for e-beam lithography and optical lithography for OPC, respectively.…”

Section: Related Workmentioning

confidence: 99%

Optical Proximity Correction Using Machine Learning Assisted Human Decision

et al. 2023

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Optical proximity correction (OPC) is a critical step in semiconductor manufacturing due to its high complexity and significant influence on the subsequent process steps. Conventional OPC using the Maxwell equation can become more and more challenging as a fully vectorized three-dimensional simulation is required for advanced technology nodes. Machine learning (ML) has been a promising alternative recently. This work proposes machine-learning-assisted human decision, which can be more in line with the clean room engineer's practice and can potentially surpass pure human decision and the pure machine learning approach. Using 10-step optimization in the photolithographic mask, the averaged mean square error (MSE) at the optimized cases are 6360 and 2101 for two-bar patterns and 7132 and 5931 for tri-line attackers when comparing pure human decision and ML-assisted human. The average MSEs at the first 3 steps are 26019 and 6023 for the two-bar pattern and 79979 and 7738 for the tri-line attacker when comparing pure ML and ML-assisted human. It is suggested that the strength of the ML-assisted human decision lies in the early-stage superiority over pure ML, flexibility, incorporating past experience, and a human sense that cannot be formulated concretely by statistical models.

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“…One node in the output layer with value 1 yields a predicted mask bias in case of classification, or a single node in the output layer may return mask bias value in regression model. This approach is very fast, e.g., more than 10 times [4] faster than MB-OPC, because correction is done just once and no lithography simulations are performed. Accuracy, however, is limited even though the model is trained well, e.g., its maximum EPE is about 4 times larger than that of MB-OPC.…”

Section: Fast Opc With ML Modelsmentioning

confidence: 99%

“…For practical application, ML-OPC may be considered as a generator of initial OPC solution, which is provided to MB-OPC to deliver the final OPC result with just a few iterations. This hybrid approach is still faster than MB-OPC alone, e.g., about 3 times [4], and is being considered as an approach for commercial use [3]. Implementation Details: A key in ML-OPC implementation is a choice of features that should be extracted from a target segment.…”

Section: Fast Opc With ML Modelsmentioning

confidence: 99%

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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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Neural Network Classifier-Based OPC With Imbalanced Training Data

Cited by 15 publications

References 26 publications

Intelligent Photolithography Corrections Using Dimensionality Reductions

Intelligent Photolithography Corrections Using Dimensionality Reductions

Optical Proximity Correction Using Machine Learning Assisted Human Decision

Computational Lithography Using Machine Learning Models

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