###### Abstract  
This paper presents an automatic optimization system for the heat source layout of multi-chip modules, referred to as IOS. The system aims to optimize the heat source layout of multi-chip modules through the integration of multiple software tools. The IOS utilizes an optimization algorithm, WINDOW DOS, and other methods to achieve this goal. Initially, two optimization strategies are introduced which are utilized in conjunction with the Non-dominated Sorting Genetic Algorithm-II(NSGA-II). Subsequently, a simplified thermal model of the multi-chip module is applied to the IOS. The automatic optimization module is then used to optimize the heat source layout. In the end, we simulated and analyzed the application results. The simulation analysis reveals that when the temperature extreme value index is optimized, the PCB board's transient temperature range falls within [343.14 K, 344.09 K]. Similarly, when the temperature homogeneity index is optimized, the PCB board's transient temperature range falls within [343.39 K, 344.21 K]. Comparing these results with the experience layout, the temperature difference is reduced by 76.36% and 79.60%, respectively. The optimization results demonstrate that the IOS has a significant impact, offering remarkable optimization effects, low cost, high efficiency, and a more uniform temperature distribution. These findings indicate that the IOS holds promising potential for applications and provides value in optimizing the heat source layout of multi-chip modules. heat source layout; multi-objective optimization; multi-software integration; NSGA-II +
Footnote †: journal: Medicalsystemic approach to enhance the quality factor of RF-integrated inductors [10]. These methods typically start with approximating a simulation using simple models, followed by the optimization of the layout of MCM, but lack simulation comparisons. [PERSON] et al. proposed a method that establishes a surrogate model of MCM and combines the HSLO optimization algorithm for layout optimization [11]. [PERSON] et al. introduced a heat source layout reverse design method based on the deep learning show, attend, and read model [12]. [PERSON] et al. presented a layout design method based on adaptive neural networks [13]. These methods have improved accuracy and assurance, but training costs continue to rise with increasing accuracy requirements. [PERSON] et al. proposed a multi-software integration method combining CAD with optimization algorithms [14]. [PERSON] applied the system engineering design tool to conceptual designs for small satellites [15]. [PERSON] et al. addressed the effective payload packaging design problem in satellite modules by proposing a hybrid multi-institution optimization method [16]. These methods utilize physical models established by simulation software which are closer to actual engineering applications and avoid the expensive costs associated with high training. This paper adopts the physical model approach. However, it is necessary to validate the optimization results through simulation and, as the model complexity and iteration times increase, the costs in terms of human labor and time also increase. [PERSON] and colleagues applied a system framework in spacecraft thermal design that significantly reduced human and time costs [17]. Therefore, this article adopts the conceptual framework of this system to redesign an automated system for thermal source layout optimization. This system aims to enhance optimization efficiency while ensuring the quality of the optimization results. This paper utilized a multi-software integration optimization method to address the heat source layout optimization problem in MCM. Firstly, a simplified thermal physics model was established, and the NSGA-II algorithm was applied for optimization. Subsequently, the optimization results were compared and validated against empirical layouts using the WINDOWS DOS command line to automate the entire optimization and validation process.