In‐Materia Annealing and Combinatorial Optimization Based on Vertical Memristive Array

Abstract

Due to its area and energy efficiency, a memristive crossbar array (CBA) has been extensively studied for various combinatorial optimization applications, from network problems to circuit design. However, conventional approaches include heavily burdening software fine‐tuning for the annealing process. Instead, this study introduces the “in‐materia annealing” method, where the inter‐layer interference of vertically stacked memristive CBA is utilized as an annealing method. When mapping combinatorial optimization problems into the configuration layer of the CBA, exponentially decaying annealing profiles are generated in nearby noise layers. Moreover, in‐materia annealing profiles can be controlled by changing compliance current, read voltage, and read pulse width. Therefore, the annealing profiles can be arbitrarily controlled and generated individually for each cell, providing rich noise sources to solve the problem efficiently. Consequently, the experimental and simulation of Max‐Cut and weighted Max‐Cut problems achieve notable results with the minimum software burden.