Mixed Ionic‐Electronic Charge Transport in Layered Black‐Phosphorus for Low‐Power Memory

Abstract

Availability of computing will be strongly limited by global energy production in 1–2 decades. Computing consumes 4–5% of global electricity supply and continues to increase. This is underpinned by memory and switching devices encountering leakage as they are downscaled. Two‑dimensional (2D) materials offer a potential solution to the fundamental problem owing to carrier confinement which significantly reduces scattering events. Herein, a mixed ionic‑electronic transport is used in layered black phosphorus (BP) based vertically stacked resistance change memories. The memory device relies on a unique interplay between the oxygen and silver ion diffusion through the stack which is generated using a combination of bottom (electrochemically active silver) and top (indium tin oxide) electrodes. The use of a transparent top‐electrode enabled for the first time to conduct spectroscopic characterization of the device and experimentally reveal fundamental mechanisms. Endurance of the devices are observed to be >104 switching cycles, with ON/OFF current ratio of >107 and standby power consumption of <5 fW, which effectively suppresses leakage current and sneak paths in a memory array. By undertaking detailed microscopic and spectroscopic investigations, supported by theoretical calculations, this work opens opportunities to enhance resistive switching performances of 2D materials for next‑generation information storage and brain‑inspired computation.

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

“…Table 1 shows the comparison of the RS performance, including SET and RESET voltages, ON/OFF ratio, retention time and endurance cycle between our device with other 2D materials‐based memristors. [ 21–37 ] It indicates that our device not only possesses relatively high ON/OFF ratio, but also ultralow SET and RESET voltages when compared with previously published works. Inspiringly, the obtained ultralow SET voltage is one order of magnitude lower than that of most reported memristors based on 2D materials.…”

“…Very recently, ultralow stand‐by power consumption of 5 fW was achieved in 2D layered black phosphorous (BP) nanosheet for memristive devices. [ 33 ] In our experiments, the memristor exhibits low power consumption in the range of <10 pW which is lower than that of most 2D materials‐based memristors, [ 28,29,36,37 ] suggesting promising low‐power applications. Furthermore, we used voltage pulse with pulse width of 1 ms and pulse amplitude of ±1 V to examine the SET and RESET speeds of device.…”

“…The memristor with vertical structure is demonstrated in Process I. When Ag electrode is positively [21] Ag/h-BN/Cu 0.72 −0.37 10 2 3 × 10 3 550 n 2016 [22] Ag/BNO x /graphene 0.7 −0.2 10 3 1.4 × 10 4 100 n 2017 [23] Graphene/MoS 2−x O x / graphene 1.1 −1.5 10 10 5 2 × 10 7 n 2018 [24] Au/Cr/MoS 2 /Cr/Au 1.4 −0.7 10 3 10 4 20 n 2018 [25] Ag/h-BN/graphene 5.2 −4 10 3 n 40 n 2019 [26] Cu/MoS 2 /Au 0.25 −0.15 4 1.8 × 10 3 20 n 2019 [27] Ag/MoS 2 /Pt 0.8 −0.6 10 3 10 5 10 6 7.35 nW 2020 [28] h-rGO/2D CMP/p-rGO/Al 2.9 −2.2 10 3 8 × 10 3 n 2.9 µW 2020 [29] Ti/h-BN/Cu 2.7 −0.86 10 9 10 4 1500 n 2020 [30] Au/Ag/MoS 2 /Si 0.4 −0.3 10 2 2 × 10 4 10 4 n 2020 [31] Ti/PdSe 2 /Au 0.73 −2 10 4 10 6 100 n 2021 [32] Ag/BP/ITO 0.39 −0.37 10 7 10 5 10 4 5 fW 2021 [33] Ti/HfSe x O y /HfSe 2 /Au 2.32 −2.5 10 3 1.5 × 10 4 40 n 2021 [34] Au/Ti/HfSe 2 /Ti/Au/Ti 0.74 −0.81 10 2 10 4 500 n 2021 [35] Au/Ni/MoO x /Mo 2 C/Ni/Au 0.5 −0.3 10 2 n 120 50 nW 2021 [36] Au/Cr/CuSe/Cr/Au 0.4 −0.4 10 2 10 4 300 11.4 µW 2022 [37] Ag/BiOI/Pt 0.05 −0.05 biased during the forming process, oxidized Ag + ions formed at the Ag electrode migrate to the Pt electrode, where Ag + ions are reduced to Ag atoms and then accumulated to grow Ag CFs (Process II), corresponding to the electroforming process. After electroforming, the device is switched from the HRS to the LRS, Ag conductive channel can be formed (Process III).…”

Hexagonal boron nitride (h-BN) memristor arrays for analog-based machine learning hardware

“…Consequently, many studies have evolved from the demonstration of isolated devices (e.g., field effect transistors or FETs) based on exfoliated flakes towards large-area methods for fabrication of integrated circuits with 2D materials [6][7][8][9][10][11][12] . While early device demonstrations focused predominantly on FET applications [13][14][15][16] , recent studies have proposed memory and neuromorphic devices based on the non-volatile resistive-switching (NVRS) behavior observed in various 2D materials including transition metal dichalcogenides (TMDC) 17 , black-phosphorus 18,19 , graphene 20,21 , hexagonal boron nitride (h-BN) [22][23][24][25][26][27][28][29][30] , etc. These devices are generally configured in vertical two-terminal structures, where the resistive switching layer is sandwiched between top and bottom metal electrodes.…”

Nonvolatile Resistive Switching in Layered InSe via Electrochemical Cation Diffusion

“…The application of an electric field perpendicular to the sample will force the oxygen ion (O 2− ) and Ag + ions to migrate in opposite directions due to their opposite charges, where the movement of the Ag or O atoms can be forward or backward according to the start and end points. [ 52 ] The relative energy of the supercell as a function of the vertical position of the Ag/O atom is depicted in Figure 4b. The barrier for diffusion of the O atom is calculated to be ≈3.7 eV, which is larger than for diffusion barrier of Ag calculated to be ≈2.3 eV.…”