Automatic clustering of wafer spatial signatures

Zhang, Wangyang; Li, Xin; Saxena, Sharad; Strojwas, Andrzej J.; Rutenbar, Rob A.

doi:10.1145/2463209.2488821

Cited by 29 publications

(5 citation statements)

References 10 publications

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“…Moreover, fuzzy matching fails to extract problematic instances on a more difficult testcase ICCAD16-4 with looser constraints that they all reach less than 50% prediction accuracy. [3,13] propose to use the frequency domain representation to sample layout patterns with similar property and detect hotspots. Here we conduct additional experiments by clustering layout clips based on their Fourier Transform results.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…For simplicity, we consider the contact hole as an example whose diffraction pattern resembles the Airy disk. The light intensity in terms of observation angle θ at the entrance of the objective lens is shown in Equation (3).…”

Section: Lithography Proximity Effectmentioning

confidence: 99%

“…Feature extraction aims to convert layout geometry information (e.g. density [1,2], frequency [3,4] and design rule [5]) into reduced mathematical representations, which are expected to improve hotspot detection accuracy. Recently, deep neural networks also exhibit powerful feature learning ability that can obtain layout representations without prior knowledge [4,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Layout pattern sampling problems are addressed by several works that are, to some extent, related to clustering approaches. Representative methods include clustering on frequency domain [3,13], Bayesian clustering [14], and clustering based on layout topology [9,10,13,15]. However, sampling and hotspot detection are mostly conducted exclusively which ignores the beneath integrity between them.…”

Section: Introductionmentioning

confidence: 99%

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Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning

Yang

Tabery³

et al. 2018

Preprint

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Layout hotpot detection is one of the main steps in modern VLSI design. A typical hotspot detection flow is extremely time consuming due to the computationally expensive mask optimization and lithographic simulation. Recent researches try to facilitate the procedure with a reduced flow including feature extraction, training set generation and hotspot detection, where feature extraction methods and hotspot detection engines are deeply studied. However, the performance of hotspot detectors relies highly on the quality of reference layout libraries which are costly to obtain and usually predetermined or randomly sampled in previous works. In this paper, we propose an active learning-based layout pattern sampling and hotspot detection flow, which simultaneously optimizes the machine learning model and the training set that aims to achieve similar or better hotspot detection performance with much smaller number of training instances. Experimental results show that our proposed method can significantly reduce lithography simulation overhead while attaining satisfactory detection accuracy on designs under both DUV and EUV lithography technologies.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: Lithography Proximity Effectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning

Yang

Tabery³

et al. 2018

Preprint

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“…Hierarchical clustering is an unsupervised machine learning technique that explores the cluster patterns of data. It proceeds in a greedy fashion such that each data sample is initialised as a cluster and the two closest clusters are merged as a new cluster at each iteration until a stopping criterion is reached [19,20].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical clustering framework for facility location selection with practical constraints

Lin

Liu

et al. 2021

IET Cyber-Phy Sys Theory & Ap

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Facility location selection plays a critical role in the planning of logistics networks. It selects the addresses of facility nodes from a candidate set of locations to optimise multiple targets such as transportation efficiency and economic cost considering the practical constraints of the real world. Thus, it is often formulated as a combinational optimisation problem, which is solved by either mixed integer programing algorithms or heuristic methods. However, these approaches are limited by several issues such as a high computational cost and weak generalisation flexibility. In this work, a novel hierarchical clustering framework is proposed for facility location selection, which can flexibly support a wide variety of optimisation targets and the combinations of multiple practical constraints that are vital in the real logistics scenarios. Beyond the original hierarchical clustering algorithm, it incorporates a looking-forward mechanism that alleviates the 'greedy trap' by utilising global information. These advantages enable the proposed method to generate reliable solutions with high time efficiency. As demonstrated by the experimental results on real JD Logistics data, the proposed method outperforms the widely adopted GGA and VNS algorithms. It also has a much lower computation cost compared to the SCIP solver, while the quality of solutions are within an acceptable range. K E Y W O R D S intelligent transportation systems, learning (artificial intelligence), optimisation, pattern clusteringThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Laplacian eigenmaps and bayesian clustering based layout pattern sampling and its applications to hotspot detection and OPC

Matsunawa

Pan

2016

2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC)

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Automatic clustering of wafer spatial signatures

Cited by 29 publications

References 10 publications

Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning

Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning

Hierarchical clustering framework for facility location selection with practical constraints

Laplacian eigenmaps and bayesian clustering based layout pattern sampling and its applications to hotspot detection and OPC

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