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research-article

Inverse Conduction Heat Transfer and Kriging Interpolation Applied to Temperature Sensor Location in Microchips

[+] Author and Article Information
Gonzalez Cuadrado David

ASME Member, School of Mechanical Engineering, Purdue University, 500 Allison Road, 47906 West Lafayette, Indiana, USA
david.gonzalez.cuadrado@gmail.com

Marconnet Amy

ASME Member, School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA
amarconn@purdue.edu

Paniagua Guillermo

ASME Member, School of Mechanical Engineering, Purdue University, 500 Allison Road, 47906 West Lafayette, Indiana, USA
gpaniagua@me.com

1Corresponding author.

ASME doi:10.1115/1.4039026 History: Received September 27, 2017; Revised December 28, 2017

Abstract

Large thermal gradients represent major operational hazards in microprocessors, hence there is a critical need to monitor possible hot spots both accurately and in real-time. Thermal monitoring in a microprocessor is typically performed using temperature sensors embedded in the electronic board. The location of the temperature sensors is primarily determined by the sensor space claim rather than the ideal location for thermal management. This manuscript presents an optimization methodology to determine the most beneficial locations for the temperature sensors inside of the microprocessors, based on input from high resolution surface infrared thermography combined with inverse heat transfer solvers to predict hot spot locations. The infrared image is used to obtain the temperature over the processor surface, and subsequently delivers the input to a 3D inverse heat conduction methodology, used to determine the temperature field within the processor. In this paper, simulated thermal maps are utilized to assess the accuracy of this method. The inverse methodology is based on a function specification method combined with a sequential regularization in order to increase accuracy in the results. Together with the number of sensors, the temperature field within the processor is then used to determine the optimal location of the temperature sensors using a genetic algorithm optimization combined with a Kriging interpolation. This combination of methodologies was validated against the Finite Element Analysis of a chip incorporating heaters and temperature sensors. An uncertainty analysis of the inverse methodology and the Kriging interpolation was performed separately to assess the reliability of the procedure.

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