Integrating Computational Fluid Dynamics and Neural Networks to Predict Temperature Distribution of the Semiconductor Chip with Multi-heat Sources

In this paper, an artificial intelligent system to predict the temperature distribution of the semiconductor chip with multi-heat sources is presented by integrating the back-propagation neural network (BNN) and the computational fluid dynamics (CFD) techniques. Six randomly generated coordinates of three power sections on the chip die are the inputs and sixty-four temperature monitoring points on the top of the chip die are the outputs. In the present methodology, one hundred sets of training data obtained from the CFD simulations results were sent to the BNN for the intelligent training. There are other sixteen generated input sets to be the test data and compared the results between CFD simulation and BNN, it shows that the BNN model is able to accurately estimate the corresponding temperature distribution as well as the maximum temperature values under different power distribution after well trained.

[1]  Hsien-Chie Cheng,et al.  Integration of Simulation and response surface methods for thermal design of multichip modules , 2004 .

[2]  Andrzej Kos.  An approach to thermal placement in power electronics using neural networks , 1993, 1993 IEEE International Symposium on Circuits and Systems.

[3]  Shyong Lee,et al.  Applications of neural networks for grading textile yarns , 2004, Neural Computing & Applications.

[4]  Avram Bar-Cohen,et al.  Design and analysis of heat sinks , 1995 .

[5]  Allan D. Kraus,et al.  Thermal Analysis and Control of Electronic Equipment , 1983 .

[6]  Ghulam Abdul Quadir,et al.  Thermal investigations of microelectronic chip with non‐uniform power distribution: temperature prediction and thermal placement design optimization , 2004 .

[7]  T.-Y.T. Lee,et al.  Thermal evaluation of a PowerPC 6201 microprocessor in a multiprocessor computer , 1996 .

[8]  C.-W. Yu,et al.  Thermal design of a desktop computer system using CFD analysis , 2001, Seventeenth Annual IEEE Semiconductor Thermal Measurement and Management Symposium (Cat. No.01CH37189).

[9]  Kankanhalli N. Seetharamu,et al.  Prediction of temperature in silicon chip with non‐uniform power: a Lagrangian interpolation approach , 2004 .

[10]  Hsin-Chung Lien,et al.  A Method of Feature Selection for Textile Yarn Grading Using the Effective Distance Between Clusters , 2002 .

[11]  Zhang Yi,et al.  Advances in Neural Networks - ISNN 2005, Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II , 2005, ISNN.

[12]  Wolfgang Rosenstiel,et al.  Error Classification and Yield Prediction of Chips in Semiconductor Industry Applications , 2000, Neural Computing & Applications.

[13]  Ralph L. Webb,et al.  Identification of Minimum Air Flow Design for a Desktop Computer Using CFD Modeling , 2001 .

[14]  Shyong Lee,et al.  Applying Pattern Recognition Principles to Grading Textile Yarns , 2002 .

[15]  Antanas Verikas,et al.  Colour classification by neural networks in graphic arts , 1998, Neural Computing & Applications.

[16]  Yeander Kuan,et al.  The Integration of the Neural Network and Computational Fluid Dynamics for the Heatsink Design , 2005, ISNN.