Multidisciplinary Placement Optimization of Heat Generating Semiconductor Logic Blocks

2008 ◽  
Author(s):  
Tohru Suwa ◽  
Hamid Hadim
Keyword(s):  
Temperature Distribution ◽  
Integrated Circuit ◽  
Element Model ◽  
Multidisciplinary Optimization ◽  
Maximum Temperature ◽  
Superposition Method ◽  
Design And Optimization ◽  
Placement Optimization ◽  
Optimization Methodology ◽  
Logic Blocks

A multidisciplinary optimization methodology for placement of heat generating semiconductor logic blocks on integrated circuit chips is presented. The methodology includes thermal and wiring length criteria, which are optimized simultaneously using the genetic algorithm. An effective thermal performance prediction methodology based on a superposition method is used to determine the temperature distribution on a silicon chip due to multiple heat generating logic blocks. Using the superposition method, the predicted temperature distribution in the silicon chip is obtained in much shorter time than with a detailed finite element model and with comparable accuracy. The main advantage of the present multidisciplinary design and optimization methodology is its ability to handle multiple design objectives simultaneously for optimized placement of heat generating logic blocks. Capabilities of the present methodology are demonstrated by applying it to several standard benchmarks. The multidisciplinary logic block placement optimization results indicate that the maximum temperature on a silicon chip can be reduced by up to 7.5°C, compared with the case in which only the wiring length is minimized.

Multidisciplinary Heat Generating Logic Block Placement Optimization Using Genetic Algorithm

2007 ◽  
Author(s):  
Tohru Suwa ◽  
Hamid Hadim
Keyword(s):  
Genetic Algorithm ◽  
Temperature Distribution ◽  
Integrated Circuit ◽  
Element Model ◽  
Multidisciplinary Optimization ◽  
Maximum Temperature ◽  
Superposition Method ◽  
Placement Optimization ◽  
Optimization Methodology ◽  
Logic Blocks

A multidisciplinary optimization methodology for placement of heat generating logic blocks on integrated circuit chips is presented. The methodology includes thermal and wiring length criteria, which are optimized simultaneously using the genetic algorithm. An effective thermal performance prediction methodology based on a superposition method is used to calculate the temperature distribution on a silicon chip due to multiple heat generating logic blocks. Using the superposition method, the predicted temperature distribution in the silicon chip is obtained in a much shorter time than with a detailed finite element model and with comparable accuracy. The main advantage of the present multi-disciplinary design and optimization methodology is its ability to handle multiple design objectives simultaneously for optimized placement of heat generating logic blocks. To demonstrate its capabilities, the present methodology is applied to benchmark cases involving placement optimization of multiple heat generating logic blocks on a silicon chip. The results indicate that the maximum temperature on a silicon chip can be reduced by up to 7.5 °C, compared with the case in which only the wiring length is minimized.

Multidisciplinary Placement Optimization of Heat Generating Electronic Components on Printed Circuit Board in Channel Flow Forced Convection Using Artificial Neural Networks

2005 ◽  
Author(s):  
Tohru Suwa ◽  
Hamid Hadim
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Forced Convection ◽  
Channel Flow ◽  
Superposition Method ◽  
Electronic Components ◽  
Printed Circuit ◽  
Placement Optimization ◽  
Optimization Methodology

A multidisciplinary placement optimization methodology for heat generating electronic components on printed circuit boards (PCBs) in channel flow forced convection is presented. In this methodology, thermal, electrical, and placement criteria involving junction temperature, wiring density, line length for high frequency signals, and critical component location are optimized simultaneously using the genetic algorithm. A board-level thermal performance prediction methodology based on channel flow forced convection boundary conditions is developed. The methodology consists of a combination of artificial neural networks (ANNs) and a superposition method that is able to predict PCB surface and component junction temperatures in a much shorter calculation time than the existing numerical methods. Three ANNs are used for predicting temperature rise at the PCB surface caused by a single heat flux at an arbitrary location on the board, while temperature rise due to multiple heat flux is calculated using a superposition method. Compact thermal models are used for the electronic components thermal modeling. Using this optimization methodology, large calculation time reduction is achieved without losing accuracy. For thermal model validation, the present thermal methodology predicts junction temperatures with maximum error of 1.8°C comparing to the conjugate solid/ fluid heat transfer analysis result. The present thermal modeling takes 12 seconds, while the conjugate analysis takes 30 hours for the validation on the same computer. To demonstrate the capabilities of the present methodology, a test case of component placement on a PCB is presented.

scholarly journals Design Synchronous Generator Using Taguchi-Based Multi-Objective Optimization

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3337
Author(s):  
Ruiye Li ◽  
Peng Cheng ◽  
Yingyi Hong ◽  
Hai Lan ◽  
He Yin
Keyword(s):  
Finite Element ◽  
Temperature Distribution ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Element Model ◽  
Optimization Method ◽  
Geometric Parameters ◽  
Maximum Temperature ◽  
Design Decision ◽  
Multi Objective

The extensive use of finite element models accurately simulates the temperature distribution of electrical machines. The simulation model can be quickly modified to reflect changes in design. However, the long runtime of the simulation prevents any direct application of the optimization algorithm. In this paper, research focused on improving efficiency with which expensive analysis (finite element method) is used in generator temperature distribution. A novel surrogate model based optimization method is presented. First, the Taguchi orthogonal array relates a series of stator geometric parameters as input and the temperatures of a generator as output by sampling the design decision space. A number of stator temperature designs were generated and analyzed using 3-D multi-physical field collaborative finite element model. A suitable shallow neural network was then selected and fitted to the available data to obtain a continuous optimization objective function. The accuracy of the function was verified using randomly generated geometric parameters to the extent that they were feasible. Finally, a multi-objective genetic optimization algorithm was applied in the function to reduce the average and maximum temperature of the machine simultaneously. As a result, when the Pareto front was compared with the initial data, these temperatures showed a significant decrease.

FEM Analysis of Temperature in High Speed Cutting of TiAl6V4 Alloys

2012 ◽  
Vol 522 ◽  
pp. 201-205
Author(s):  
You Xi Lin ◽  
Cong Ming Yan ◽  
Zheng Ying Lin
Keyword(s):  
Temperature Distribution ◽  
High Speed ◽  
Metal Cutting ◽  
Cutting Temperature ◽  
Fem Analysis ◽  
Element Model ◽  
Maximum Temperature ◽  
Depth Of Cut ◽  
Machined Surface ◽  
Radial Depth

mprovements in modeling and simulation of metal cutting processes are required in advanced manufacturing technologies. A three dimensional fully thermal mechanical coupled finite element model had been applied to simulate and analyze the cutting temperature for high speed milling of TiAl6V4 titanium alloy. The temperature distribution induced in the tool and the workpiece was predicted. The effects of the milling speed and radial depth of cut on the maximum cutting temperature in the tool was investigated. The results show that only a rising of temperature in the lamella of the machined surface is influenced by the milling heat. The maximum temperature in the tool increases with increasing radial depth of cut and milling speed which value is 310°C at a speed of 60 m/min and increases to 740°C at 400m/min. The maximum temperature is only effective on a concentrated area at the cutting edge and the location of the maximum temperature moves away from the tool tip for higher radial depths of milling. The predicted temperature distribution during the cutting process is consistent with the experimental results given in the literature. The results obtained from this study provide a fundamental understanding the process mechanics of HSM of TiAl6V4 titanium alloys.

scholarly journals Numerical Simulation of the Temperature on the Metallization of an Integrated Circuit and its Impact on Interconnect Lifetime

2020 ◽  
Vol 15 (2) ◽  
pp. 1-5
Author(s):  
Rafael Oliveira Nunes ◽  
J. L. R. Bohorquez ◽  
R. L. De Orio
Keyword(s):  
Numerical Simulation ◽  
Finite Element ◽  
Temperature Distribution ◽  
Integrated Circuit ◽  
Power Dissipation ◽  
Operational Amplifier ◽  
Temperature Increase ◽  
Element Model ◽  
Design Stage ◽  
The Impact

This paper demonstrates a finite element model to investigate the temperature change of the interconnects of an integrated circuit due to the power dissipation of the transistors in the substrate. The temperature of the local interconnect is more significantly affected, exhibiting an increase of 49 K and 34 K, for the Metal 1 and Metal 2, respectively. We discuss the impact of the temperature increase in the electromigration and, as a consequence in the lifetime of an operational amplifier, which demonstrates the importance of considering the metallization temperature distribution in the design stage.

Thermal Design and Optimization Methodology for Integrated Power Electronics Modules

2005 ◽  
Vol 127 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Ying Feng Pang ◽  
Elaine P. Scott ◽  
Jonah Zhou Chen ◽  
Karen A. Thole
Keyword(s):  
Temperature Rise ◽  
Design Methodology ◽  
Three Dimensional ◽  
Thermal Design ◽  
Maximum Temperature ◽  
Percent Reduction ◽  
Design And Optimization ◽  
Overall Performance ◽  
The Common ◽  
Optimization Methodology

A methodology was developed and implemented to optimize the design layout for i_ntegrated p_ower e_lectronics m_odules (IPEMs) by considering both the electrical and thermal performances. This paper is primarily focused on the thermal aspects, which were analyzed using three-dimensional (3D) computational software tools. Implementation of the design methodology resulted in a 70 percent reduction in the common mode current, a 4 percent reduction in the size of the geometric footprint, and a 7°C reduction in the maximum temperature rise for the case studied, thus, providing an increase in the IPEM’s overall performance.

scholarly journals Thermal Simulation of Power Lithium-ion Battery Module

2021 ◽  
Vol 233 ◽  
pp. 01028
Author(s):  
Fancong Zeng ◽  
Zhijiang Zuo ◽  
Han Li ◽  
Libo Pan
Keyword(s):  
Temperature Distribution ◽  
Lithium Ion Battery ◽  
Thermal Management ◽  
Lithium Ion ◽  
Maximum Temperature ◽  
Inlet Temperature ◽  
Design And Optimization ◽  
Air Inlet ◽  
Cfd Method ◽  
Battery Module

Thermal management of power lithium-ion battery modules is very important to avoid thermal problems such as overheating and out of control, the study of thermal behavior of battery modules can provide guidance for the design and optimization of modules and thermal management. In this paper, a 3d thermal model of the power lithium-ion battery module is established based on STARCCM+ by using computational fluid dynamics (CFD) method, and a grid independence simulation test is used to determine the number of grids, the temperature distribution is analyzed under the condition of 1C charge current. The research results show that the internal temperature rises gradually with the charge going on, the temperature distribution of the cells is basically symmetrical. When the heat transfer coefficient is 5W/(m2⋅K) and the natural convective air inlet temperature is 300K, the module temperature uniformity is good. But because of the maximum temperature slightly higher than the temperature of thermal runaway, additional cooling methods need to be considered to cool the battery.

Novel Design of Thermal-Via Configurations for Collector-Up HBTs

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Pei-Hsuan Lee ◽  
Hsien-Cheng Tseng ◽  
Jung-Hua Chou
Keyword(s):  
Finite Element ◽  
Temperature Distribution ◽  
Heterojunction Bipolar Transistors ◽  
Heat Dissipation ◽  
Element Model ◽  
Bipolar Transistors ◽  
Maximum Temperature ◽  
3D Analysis ◽  
2D And 3D ◽  
Novel Design

We devise a finite-element model to analyze the thermal performance of collector-up (C-up) heterojunction bipolar transistors (HBTs) with a thermal-via configuration. A demonstration on the GaInP/GaAs C-up HBT is presented in this Brief, and the novelty of this work is that both 2D and 3D temperature-distribution analyses are performed. The 2D results indicate that the original thermal-via configuration can be reduced by 29%. Furthermore, the results show that the maximum temperature within the collector calculated from 3D analysis is lower than that from the 2D analysis. Based on the 3D analysis, it is revealed that the reported configuration can be reduced by 32%. Therefore, the C-up HBT with a compact thermal-via should be helpful for miniaturization of heat-dissipation packaging configurations within HBT-based high-power amplifiers.

Multidisciplinary Placement Optimization of Heat Generating Electronic Components on Printed Circuit Boards

2006 ◽  
Vol 129 (1) ◽  
pp. 90-97 ◽  
Author(s):  
Tohru Suwa ◽  
Hamid Hadim
Keyword(s):  
Genetic Algorithm ◽  
Printed Circuit Boards ◽  
Line Length ◽  
Junction Temperature ◽  
Superposition Method ◽  
Circuit Boards ◽  
Electronic Components ◽  
Printed Circuit ◽  
Placement Optimization ◽  
Optimization Methodology

A multidisciplinary placement optimization methodology for heat generating electronic components on printed circuit boards (PCBs) is presented. The methodology includes thermal, electrical, and placement criteria involving junction temperature, wiring density, line length for high frequency signals, and critical component location which are optimized simultaneously using the genetic algorithm. A board-level thermal performance prediction methodology which is based on a combination of a superposition method and artificial neural networks is developed for this study. Two genetic algorithms with different thermal prediction modules are used in a cascade in the optimization process. The first genetic algorithm uses simplified thermal network modeling and it is mainly aimed at finding component locations that avoid any overlap. Compact thermal models are used in the second genetic algorithm leading to more accurate thermal prediction which improves the placement optimization obtained using the first algorithm. Using this optimization methodology, large calculation time reduction is achieved without losing accuracy. To demonstrate its capabilities, the present methodology is applied to a test case involving placement optimization of several heat generating electronics components on a PCB.

Reliability Based Design Optimization Framework for Boat Propeller

2015 ◽  
Vol 1099 ◽  
pp. 94-101
Author(s):  
A. Makhloufi ◽  
M. Mansouri ◽  
Bouchaib Radi ◽  
Abdelkhalak El Hami
Keyword(s):  
Probabilistic Approach ◽  
Simulation Models ◽  
Element Model ◽  
Local Optimum ◽  
Design And Optimization ◽  
Reliability Based Design ◽  
Engineering Designs ◽  
Optimization Methodology ◽  
Coupled Structures ◽  
Optimum Solution

The need for improvements in engineering designs especially for coupled structures is nowadays becoming a major industry request. Today there is a desire to perform optimizations in order to receive optimal system properties. However, for computationally expensive simulation models, an optimization may be too tedious to be motivated. Deterministic approaches are unable to take into account all the variability’s that characterize design input properties without leading to oversized structures. The objectives of this work are to quantify the influence of material and operational uncertainties on the performance of structures coupled with fluid, and to develop a reliability-based design and optimization methodology for this type of the structures. Such a problem requires a very high computation cost, which is mainly due to the calculation of gradients, especially when a finite element model is used. To simplify the optimization problem and to find at least a local optimum solution, a new method based on semi-numerical solution is proposed in this paper. The results demonstrate the viability of the proposed reliability-based design and optimization methodology relative to the classical methods, and demonstrate that a probabilistic approach is more appropriate than a deterministic approach for the design and optimization of structures coupled with fluid

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