Machine learning (ML)-guided OPC using basis functions of polar Fourier transform

Choi, Suhyeong; Shim, Seongbo; Shin, Youngsoo

doi:10.1117/12.2219073

Cited by 15 publications

(11 citation statements)

References 2 publications

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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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Section: Related Workmentioning

confidence: 99%

Optical Proximity Correction Using Machine Learning Assisted Human Decision

et al. 2023

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“…This leads to overfitting and, consequently, reduced accuracy. Choi et al [186] proposed the usage of basic functions of polar Fourier transform (PFT) as parameters of ML OPC. Here, the PFT signals obtained from the layout are used as input parameters for a MLP whose number of layers and neurons are decided empirically.…”

Section: A Ai For Lithographymentioning

confidence: 99%

AI/ML Algorithms and Applications in VLSI Design and Technology

Amuru¹,

Vudumula²,

K.³

et al. 2022

Preprint

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An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and dataintensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations.

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“…4 ML-OPC using MLP (multilayer perceptron) model [4]. c 2021 Information Processing Society of Japan lar Fourier transform (PFT) signals can substantially reduce the number of features [7]. As will be shown in Section 4, a PFT signal, which is a convolution of PFT basis function (or optical kernel function) and local layout centered at target segment, is a component in light intensity calculation and thus well represents light interference around the segment.…”

Section: Fast Opc With ML Modelsmentioning

confidence: 99%

Computational Lithography Using Machine Learning Models

Shin

2021

IPSJ Transactions on System LSI Design Methodology

Self Cite

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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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Machine learning (ML)-guided OPC using basis functions of polar Fourier transform

Cited by 15 publications

References 2 publications

Optical Proximity Correction Using Machine Learning Assisted Human Decision

Optical Proximity Correction Using Machine Learning Assisted Human Decision

AI/ML Algorithms and Applications in VLSI Design and Technology

Computational Lithography Using Machine Learning Models

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