Abstract
Thermoelectric effects can be harnessed in a vertical-pillar phase-change memory cell by the inclusion of geometry-level and material-level asymmetry to reduce the programming current. However, the trade-off between the programming current and SET resistance of a phase-change memory cell makes their optimization vital for a predictive overall performance. We propose herein an analytical model to include thermoelectric effects in a phase-change memory and optimize the cell using a nature-inspired multiobjective constrained optimization algorithm. The model is then analyzed for a wide range of geometries and material parameters to study the collective effect on the cell performance. Furthermore, we suggest an optimal configuration for the hybrid phase-change memory cell.
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