<p dir="ltr">The adoption of Heterogeneous Integration (HI) technologies in semiconductor packaging to build 2.5D/3D structures has led to increased power densities and material heterogeneity. These structures place a new burden on thermal and mechanical design. Additionally, these structures allow for significantly increased physical design freedom. With more possible layout options as well as tougher thermal constraints, new specialized tools are required to accelerate this type of design.</p><p dir="ltr">To address this problem using traditional finite element analysis Stack3D is presented. Stack3D is a steady-state thermal-mechanical geometry modeling and analysis platform for advanced packaging early design exploration. It is a finite element simulator developed from scratch in Matlab complete with symbolic geometry representation, automatic meshing, chip power map support, and sparse matrix acceleration.</p><p dir="ltr">After the development of Stack3D, methods for further accelerating the simulation process at the cost of solution accuracy were examined. Neural networks were selected as an engine for this task based on their millisecond evaluation time. In order to choose between the training paradigms of Physics Informed and Data Driven neural networks, a series of benchmarks were run to identify Data Driven networks as ideal candidates for steady state heat conduction.</p><p dir="ltr">Last, the first neural network model for fully generalized steady state heat conduction of 3D packages is developed. This is made possible by using the solution to the partial differential equation governing steady state heat conduction and casting the problem into an image-to-image translation framework. After accounting for the third spatial dimension, this allows the use of cutting edge image processing network for the heat conduction problem. After training, the network was able to run tens of thousands of simulations with an average of 0.53\% error and 0.0035 seconds per simulation.</p>