On the integration of 3D IC design, thermal management issues play a significant role. So, it is required to implement an effective approaches and solutions for integrating 3DIC. The TSV causes problems with the distinct coefficients of thermal expansion that induces mismatch strains and stresses. The major drawback of 3DIC is the thermal management issues which increases the power consumption through the current crowding, perhaps the temperature upraised by the slacked layers due to its heat generation. Several research has not been undergone in 3DIC utilizing machine learning approaches which is highly complicated. This paper firstly proposes an efficient ML model to achieve better reduction in wire length and temperature. An efficient linear regression model is preferred here in order to achieve significant performances in TSV layer assignment. The linear regression utilized gradient based approach where the error is predicted at every instance through tracing gradient cost function. An optimized TSV layer assignment is achieved with this flexible ELRM. The performance analysis of data shows that the proposed ELRM based TSV assignment achieved better wire length and temperature. The ISPD98 Circuit Benchmark Suite is utilized for result evaluation and it achieves improved TSV layer assignment through reducing wire length and temperature.

在3D IC设计的集成中，热管理问题起着重要作用。因此，需要实施一种有效的方法和解决方案来集成3DIC。TSV会引起热膨胀系数不同的问题，从而引起不匹配的应变和应力。3DIC的主要缺点是热管理问题，该问题会因电流拥挤而增加功耗，也许是由于发热导致松弛层温度升高所致。利用高度复杂的机器学习方法在3DIC中尚未进行一些研究。本文首先提出了一种有效的机器学习模型，以更好地减少导线长度和温度。为了在TSV层分配中获得显着性能，此处首选高效的线性回归模型。线性回归利用基于梯度的方法，其中通过跟踪梯度成本函数在每个实例中预测误差。通过这种灵活的ELRM，可以实现优化的TSV层分配。数据性能分析表明，基于ELRM的TSV分配方案可实现更好的导线长度和温度。ISPD98电路基准套件用于结果评估，并通过减少导线长度和温度来改善TSV层分配。