Exploring Low Power and Ultrafast Memristor on p-Type van der Waals SnS

Abstract

Memristor devices that exhibit high integration density, fast speed, and low power consumption are candidates for neuromorphic devices. Here, we demonstrate a filament-based memristor using p-type SnS as the resistive switching material, exhibiting superlative metrics such as a switching voltage ∼0.2 V, a switching speed faster than 1.5 ns, high endurance switching cycles, and an ultralarge on/off ratio of 10 8 . The device exhibits a power consumption as low as ∼100 fJ per switch. Chip-level simulations of the memristor based on 32 × 32 high-density crossbar arrays with 50 nm feature size reveal on-chip learning accuracy of 87.76% (close to the ideal software accuracy 90%) for CIFAR-10 image classifications. The ultrafast and low energy switching of p-type SnS compared to n-type transition metal dichalcogenides is attributed to the presence of cation vacancies and van der Waals gap that lower the activation barrier for Ag ion migration.

A Robust Memristor Based on Epitaxial Vertically Aligned Nanostructured BaTiO3−CeO2 Films on Silicon

“…In simulation, the top‐level architecture includes tiles, global buffers, neural function peripherals, and weight gradient calculation functions. [ 38 ] The tile also contains multiple processing elements (PEs), which can be further divided into Transpose Synaptic Arrays, and can support training in the NeuroSim core, as shown in Figure 7c. In the readout mode of the BTO−CeO 2 ‐based array simulation, we adopt parallel readout, the structure of which is shown in Figure 7d.…”

Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing

“…Lu et al reported Ag/SnS/Pt memristor devices that allow for robust facile Ag filament formation attributed to the presence of Sn vacancies (Fig. 4i) [140]. In contrast to most TMCs, which are intrinsically n-type due to chalcogen vacancies, SnS is an intrinsically p-type semiconductor due to Sn vacancies [145].…”

High‐Performance Memristor Based on 2D Layered BiOI Nanosheet for Low‐Power Artificial Optoelectronic Synapses

“…The measured current is ≈100 pA at the read voltage of 0.01 V. Accordingly, the energy consumption for the fabricated memristor is ≈100 fJ per switch, suggesting low power applications from our BiOI memristive device. [ 34,38 ]…”

“…The measured current is ≈100 pA at the read voltage of 0.01 V. Accordingly, the energy consumption for the fabricated memristor is ≈100 fJ per switch, suggesting low power applications from our BiOI memristive device. [34,38] In order to further understand the conduction mechanism of the memristor, the positive part of the I-V curve in Figure 2b is re-plotted in double logarithmic scales (Figure 2c). For the LRS, the fitted slope is 1.030, suggesting the ohmic conduction mechanism.…”