Energy-Efficient Integrated Electro-Optic Memristors.

He, Yuhan; Farmakidis, Nikolaos; Aggarwal, Samarth; Dong, Bowei; Lee, June Sang; Wang, Mengyun; Zhang, Yi; Parmigiani, Francesca et al. · Nano Lett · 2024

basic_science · Level V

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Abstract

Neuromorphic photonic processors are redefining the boundaries of classical computing by enabling high-speed multidimensional information processing within the memory. Memristors, the backbone of neuromorphic processors, retain their state after programming without static power consumption. Among them, electro-optic memristors are of great interest, as they enable dual electrical-optical functionality that bridges the efficiency of electronics and the bandwidth of photonics. However, efficient, scalable, and CMOS-compatible implementations of electro-optic memristors are still lacking. Here, we devise electro-optic memristors by structuring the phase-change material as a nanoscale constriction, geometrically confining the electrically generated heat profile to overlap with the optical field, thus achieving programmability and readability in both the electrical and optical domains. We demonstrate sub-10 pJ electrical switching energy and a high electro-optical modulation efficiency of 0.15 nJ/dB. Our work opens up opportunities for high-performance and energy-efficient integrated electro-optic neuromorphic computing.