All-Photonic in-Memory Computing Based on Phase-Change Materials
Carlos Ríos, Nathan Youngblood, et al.
CLEO 2019
Nanoscale resistive memory devices are being explored for neuromorphic and in-memory computing. However, non-ideal device characteristics of read noise and resistance drift pose significant challenges to the achievable computational precision. Here, it is shown that there is an additional non-ideality that can impact computational precision, namely the bias-polarity-dependent current flow. Using phase-change memory (PCM) as a model system, it is shown that this “current–voltage” non-ideality arises both from the material and geometrical properties of the devices. Further, we discuss the detrimental effects of such bipolar asymmetry on in-memory matrix-vector multiply (MVM) operations and provide a scheme to compensate for it.
Carlos Ríos, Nathan Youngblood, et al.
CLEO 2019
Geethan Karunaratne, Abbas Rahimi, et al.
AICAS 2021
Corey Liam Lammie, Hadjer Benmeziane, et al.
Nat. Rev. Electr. Eng.
Manuel Le Gallo, Daniel Krebs, et al.
Advanced Electronic Materials