Machine learning (ML)-based lithography optimizations

Shim, Seongbo; Choi, Suhyeong; Shin, Youngsoo

doi:10.1109/apccas.2016.7804021

Cited by 7 publications

(4 citation statements)

References 9 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 model shows a drastic speedup in computation with less error. Shim et al [191] used decision trees and logistic regression for SRAF generation, which showed a 10× improvement in runtime. Etching and mask synthesis are performed simultaneously.…”

Section: A Ai For Lithographymentioning

confidence: 99%

“…Recently, ML has been used in etching to predict the etch bias (over-etched or under-etched). ANNs [191], [192] have been used for the prediction of the etch proximity correction to compensate for the etch bias, yielding better accuracy than traditional methods.…”

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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“…Most data in the semiconductor fields, however, is not available due to high measurement costs. Thus, data-driven approaches have been applied to limited applications such as chip design automation, 3) test vector generation, 4) process optimization, 5) and defect detection. 6,7) Such studies have something in common they can gather massive data, readily.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive studies on deep learning applicable to TCAD

Myung

Choi

Jang

et al. 2023

Jpn. J. Appl. Phys.

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TCAD simulation has incessantly solved many complex problems, but it becomes demanding that alternatives be found because TCAD simulation cannot provide the precise and fast prediction in the nano-scale era. With the success story on deep learning in research area, many big data companies have attempted to introduce deep learning to support or replace TCAD simulation. The reason is deep learning models has great potential that solves the problems of the TCAD simulation in terms of execution time, coverage. This paper aims to describe various scenarios of deep learning applicable to TCAD. We firstly describe an application that supplies TCAD data to the deep learning model although TCAD simulation is not calibrated. We then review various approaches that mimic TCAD simulation itself. We finally introduce an application that deep learning model automatically calibrates TCAD models to the measurement without experts. In each scenario, we review the related papers and compare pros and cons.

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Machine learning (ML)-based lithography optimizations

Cited by 7 publications

References 9 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

Comprehensive studies on deep learning applicable to TCAD

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