Intelligent model-based OPC

Huang, Wen‐Shih; Lai, Ching-Ming; Luo, Boren; Tsai, Cheng‐Kun; Chih, Mingchang; Lai, Chien-Wen; Kuo, C. C.; Liu, R.G.; Lin, Hon-Jarn

doi:10.1117/12.657792

Cited by 13 publications

(11 citation statements)

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“…Recently, Huang el al. [16] have proposed a similar idea as the one presented here to increase model based OPC convergence rate. Similar to the prior work, Huang el al.…”

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confidence: 99%

Optical Proximity Correction With Linear Regression

Zakhor

2008

IEEE Trans. Semicond. Manufact.

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An important step in today's Integrated Circuit (IC) manufacturing is optical proximity correction (OPC). In model based OPC, masks are systematically modified to compensate for the non-ideal optical and process effects of optical lithography system. The polygons in the layout are fragmented, and simulations are performed to determine the image intensity pattern on the wafer. If the simulated pattern on the wafer does not match the desired one, the mask is perturbed by moving the fragments.This iterative process continues until the pattern on the wafer matches the desired one. Although OPC increases the fidelity of pattern transfer to the wafer, it is quite CPU intensive due to the simulations performed at each iteration. In this paper, linear regression techniques from statistical learning are used to predict the fragment movements. The goal is to reduce the number of iterations required in model based OPC by using a fast, computationally efficient linear regression solution as the initial guess to model based OPC. Experimental results show that fragment movement predictions via linear regression model significantly decrease the number of iterations required in model based OPC, thereby decreasing the product development time in I.C. design and manufacturing.

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“…Recently, Huang el al. [16] have proposed a similar idea as the one presented here to increase model based OPC convergence rate. Similar to the prior work, Huang el al.…”

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confidence: 99%

Optical Proximity Correction With Linear Regression

Zakhor

2008

IEEE Trans. Semicond. Manufact.

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“…Network. An ANN simulates brain function through mathematical functions (Fyfe 2005) and aims to solve machine-learning problems by combining a large amount of processing elements (neurons) that are highly interconnected and work together (Huang et al 2006). Generally, an ANN consists of three types of artificial neurons-input, hidden, and output neurons-which are organized in layers.…”

Section: Artificial Neuralmentioning

confidence: 99%

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

et al. 2018

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Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions. CCS-Concepts: Human-centered computing → Ubiquitous and mobile computing; General and reference → Surveys and overviews; Computing methodologies → Machine learning algorithms

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“…A few new convergence schemes were proposed to improve the convergence rate [4]. A neural network was proposed to give a better initial guess which results in lower number of OPC iterations [5]. The runtime implications of some OPC parameters have been shown in [6], which pointed out the potential trade-off between various parameters for runtime.…”

Section: Introductionmentioning

confidence: 99%

A novel intensity based optical proximity correction algorithm with speedup in lithography simulation

Pan

2007

2007 IEEE/ACM International Conference on Computer-Aided Design

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It is important to reduce the Optical Proximity Correction (OPC) runtime while maintaining a good result quality. In this paper, we obtain a better formula, which theoretically speeds up the widely used method, Optimal Coherent Approximations (OCA's), by a factor of 2×. We speed up the OPC algorithm further by making it intensity based (IB-OPC), because it requires much less intensity simulations than the conventional Edge Placement Error (EPE) based OPC algorithms. In addition, the IB-OPC algorithm, which uses the efficiently computed sensitivity information, converges faster than the EPE based OPC. Our IB-OPC experimental results show a runtime speedup of up to 15× with a comparable result quality as of the EPE based OPC.

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Intelligent model-based OPC

Cited by 13 publications

References 0 publications

Optical Proximity Correction With Linear Regression

Optical Proximity Correction With Linear Regression

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

A novel intensity based optical proximity correction algorithm with speedup in lithography simulation

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