  Leveraging the latent heat of phase change materials (PCMs) can reduce the
peak temperatures and transient variations in temperature in electronic
devices. But as the power levels increase, the thermal conduction pathway from
the heat source to the heat sink limits the effectiveness of these systems. In
this work, we evaluate embedding the PCM within the silicon device layer of an
electronic device to minimize the thermal resistance between the source and the
PCM to minimize this thermal resistance and enhance the thermal performance of
the device. The geometry and material properties of the embedded PCM regions
are optimized using a combination of parametric and machine learning
algorithms. For a fixed geometry, considering commercially available materials,
Solder 174 significantly outperforms other organic and metallic PCMs. Also with
a fixed geometry, the optimal melting points to minimize the peak temperature
is higher than the optimal melting point to minimize the amplitude of the
transient temperature oscillation, and both optima increase with increasing
heater power. Extending beyond conventional optimization strategies, genetic
algorithms and particle swarm optimization with and without neural network
surrogate models are used to enable optimization of many geometric and material
properties. For the test case evaluated, the optimized geometries and
properties are similar between all ML-assisted algorithms, but the
computational time depends on the technique. Ultimately, the optimized design
with embedded phase change materials reduces the maximum temperature rise by
19% and the fluctuations by up to 88% compared to devices without PCM.
