Process variation analysis employing artificial neural networks

Davis, W. Bradford

doi:10.1109/ijcnn.1992.226998

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Davis (1992) employed an artificial neural network (ANN) average model and an ANN noise model to learn a pure Gaussian bump with spatially varying Gaussian noise. That is, the process variation information was not captured.…”

Section: Introductionmentioning

confidence: 99%

MLP neural network models of CMM measuring processes

Liao

1996

J Intell Manuf

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The objective of this study is to show how a multi-layer perceptron (MLP) neural network can be used to model a CMM measuring process. To date, most MLP-based process models have been established for process mean only. An innovative approach is proposed to model simultaneously the mean and the variation of a CMM process using one integrated MLP architecture. Therefore, the MLP-based model obtained captures not only the process mean but also the process variation information. Selected issues related to neural network training are also discussed. Specifically, the guideline that was proposed by Mirchandani and Cao (1989) for selecting a number of hidden neurons is tested to determine the effects of the number of hidden neurons. The performances of two different learning algorithms -back-propagation with momentum factor (BPM) and the Fletcher-Reeves (FR) algorithm -are studied in terms of CPU time, training error, and generalization error.

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Section: Introductionmentioning

confidence: 99%

MLP neural network models of CMM measuring processes

Liao

1996

J Intell Manuf

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Diagnose und Prognose mit Neuronalen Netzen

Heuer¹

1997

Neuronale Netze in Der Industrie

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Neural networks for the design and fabrication of integrated circuits for aerospace applications

Creech¹

1997

1997 IEEE Aerospace Conference

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A great effort has been underway in recent years to develop microwave and millimeter wave monolithic integrated circuit (MIMIC) technology for phased array radar systems for airborne and space based applications. Solid-state MIMICS have made a dramatic impact on capability, reliability, and cost of these sensor systems. However, the computer-aided engineering (CAE) tools necessary for the modeling and simulation of such complex systems has not kept pace. This paper presents a methodology for the utilization of neurocomputing technology to achieve enhanced yield and efficient yield prediction in integrated circuit (IC) manufacturing. Specifically, artificial neural networks (ANNs) are employed to model complex relationships between material and device characteristics at critical stages of the semiconductor fabrication process. Additionally, a methodology for the development of ANN models of MIMIC passive elements based on the elements physical parameters is described. Results are presented which illustrate that a ANN can predict the equivalent circuit parameters (ECPs) and/or the scattering-parameters of these passive elements to nearly the same degree of accuracy as that afforded by electromagnetic (EM) simulation.

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Process variation analysis employing artificial neural networks

Cited by 4 publications

References 4 publications

MLP neural network models of CMM measuring processes

MLP neural network models of CMM measuring processes

Diagnose und Prognose mit Neuronalen Netzen

Neural networks for the design and fabrication of integrated circuits for aerospace applications

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