Multidisciplinary Placement Optimization of Heat Generating Electronic Components on a Printed Circuit Board in an Enclosure

Abstract

A multidisciplinary placement optimization methodology for heat generating electronic components on a printed circuit board (PCB) subjected to forced convection in an enclosure 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 source at an arbitrary location on the board, while temperature rise due to multiple heat sources 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. To demonstrate its capabilities, the present methodology is applied to a test case involving multiple heat generating component placement optimization on a PCB.Index Terms-Artificial neural networks (ANNs), electronic component placement optimization, genetic algorithm, printed circuit board (PCB) thermal analysis.

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

“…In generating the optimal Pareto sets of solution, two conflicting objective functions have been identified (Suwa and Hadim 2007) which are thermal f (T ) and area f (A) functions. The result (Fig.…”

“…The optimization of fitness function F(T, A, P, L) requires the optimization of the four objective functions. It has been shown that these objective functions are conflicting in nature (Vassighi and Sachdev 2006;Suwa and Hadim 2007;Masana 2007). In order to use the weighted sum approach (Hajela and Lin 1992;Murata and Ishibuchi 1995), these objective functions can be combined into a scalar fitness function given in Eq.…”

Electronic component placement optimization for heat measures of smartglasses

“…Various efforts have been made to address thermal issues by optimizing the placement of heat-generating electronic components. Thermal aware floor planning methods for VLSI [7][8][9][10][11] and placement optimizing methods for electronic components on printed circuit boards (PCBs) [12][13][14][15][16][17][18][19] using a genetic algorithm (GA) or its derivations like the fuzzy GA (FGA), the inverse GA (IGA), and the elitist non-dominated sorting genetic algorithm (NSGA-II) [20,21] have been proposed. Methods using the simulated annealing (SA), the mixed integer liner programming (MILP), the matrix synthesis problem (MSP), the ant colony optimization algorithm (ACO), the firefly optimization, and the Bayesian optimization (BO) has also been proposed [22][23][24][25][26][27].…”

Thermal placement on PCB of components including 3D ICs

“…Placement optimization of chips in SoP designs has been presented [27]. In PCB designs, thermal placement techniques for components have been presented [28,29,30,31]. For satisfying objective functions (e.g., temperature or power), genetic algorithms, ant colony algorithms, etc.…”

“…However, some components can be placed freely. Most technical papers regarding the thermal placement of components on a PCB handle heat-generating electronic components only [28,29,30,31]. They mainly deal with the optimal thermal placement of only IC packages on a PCB.…”