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The prediction of maximum temperature for single chips’ cooling using artificial neural networks

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Abstract

A CFD simulation usually requires extensive computer storage and lengthy computational time. The application of artificial neural network models to thermal management of chips is still limited. In this study, the main objective is to find a neural network solution for obtaining suitable thickness levels and material for a chip subjected to a constant heat power. To achieve this aim a neural network is trained and tested using the results of the CFD program package Fluent. The back-propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function is employed in the network. By using the weights of the network, various formulations are designed for the output. The network has resulted in R 2 values of 0.999, and the mean% errors smaller than 0.8 and 0.7 for the training and test data, respectively. The analysis is extended for different thickness and input power values. Comparison of some randomly selected results obtained by the neural network model and the CFD program has yielded a maximum error of 1.8%, mean absolute percentage error of 0.55% and R 2 of 0.99994.

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Abbreviations

ANN:

Artificial neural network

Au:

Gold

CFD:

Computational fluid dynamics

Cu:

Copper

FVM:

Finite volume method

IC:

Integrated circuit

LM:

Levenberg–Marquardt

MAP:

Mean absolute percentage

RMS:

Root mean squared

SCG:

Scaled conjugate gradient

Si:

Silicon

C p :

Specific heat

k :

Heat transfer coefficient

q′′ :

Heat power

R 2 :

Fraction of variance

t 1 :

Thickness of lower plate

t 2 :

Thickness of upper plate

T :

Temperature (K)

ρ :

Density

x, y, z:

Rectangular coordinates

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Authors and Affiliations

  1. Engineering and Architecture Faculty, Gazi University, 06503, Ankara, Turkey

    Abuzer Ozsunar

  2. Engineering Faculty, Kırıkkale University, 71450, Kirikkale, Turkey

    Erol Arcaklıoglu

  3. Ziraat Bank, Ankara, Turkey

    F. Nusret Dur

Authors
  1. Abuzer Ozsunar
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  2. Erol Arcaklıoglu
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  3. F. Nusret Dur
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Corresponding author

Correspondence to Abuzer Ozsunar.

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Ozsunar, A., Arcaklıoglu, E. & Nusret Dur, F. The prediction of maximum temperature for single chips’ cooling using artificial neural networks. Heat Mass Transfer 45, 443–450 (2009). https://doi.org/10.1007/s00231-008-0445-x

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  • DOI: https://doi.org/10.1007/s00231-008-0445-x

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