Deep Insight into Steep‐Slope Threshold Switching with Record Selectivity (>4 × 10¹⁰) Controlled by Metal‐Ion Movement through Vacancy‐Induced‐Percolation Path: Quantum‐Level Control of Hybrid‐Filament

Abstract

This study demonstrates a hyper‐level control of metal‐ion migration through vacancy‐induced‐percolation (VIP) path to maximize the steep‐slope performance of the threshold selector with excellent selectivity and endurance. Highly efficient control over metal‐ion migration through VIP is achieved with sophisticated stack engineering through the material evolution process and refined electrical operation. A thorough analysis of the energetics of metal‐ion‐ and vacancy‐based hybrid filament is performed using density functional theory calculations, which leads to a proper tuning of the biasing scheme. Command over bias‐induced ion migration to control the atomic‐scale filament is the key to maximize the threshold switching (TS) performance. The devices are designed with different diffusive materials and alloys. Precise atomic‐level control is realized with optimized device structure and alloy electrode, which indicates that the TS is safe within the vicinity of quantum contact. Owing to the synergistic impact of localized metal‐ion injection through VIP, alloy electrode material, and atomic‐level filament, the devices exhibit meticulous control of a highly stable TS with a high steep‐slope of <2 mV dec−1, extremely high selectivity of >4 × 1010 with an exceptional endurance >109 cycles, and device yield of 100%, which suggest the applicability of these devices in 1S1R and 1T1R platforms.

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

“…For circuit simulation-oriented models, this adaptation capability must be achieved by means of a few numbers of simple and robust equations driven by a reduced set of parameters, if possible, with physical origin, if not, with some degree of electrical meaning. This is the signature of a compact behavioral approach, in which the central objective is not to outperform the microscopic level models (for instance the kinetic Monte-Carlo and Finite Element models such as those reported in [1][2][3][4]) in terms of accuracy and fidelity to the device physics, but matching observations and simulations as close as possible. Clearly, accurate representation of the electron transport across the investigated device under arbitrary input signals is two-fold: first, it encourages the design and assessment of more complex circuits and systems, and second, it allows to identify and establish links among the elementary modeling pieces that lead to the variety of observed behaviors (conduction characteristics).…”

Application of Resistive Random Access Memory in Hardware Security: A Review

“…The interdependency of selectivity and endurance is reported recently. [72] A selectivity >10 10 can be achieved but a selectivity of 10 8 is suitable to switch for longer cycles. In the quest for a hybrid filament, the amount of V o can play an important role.…”

Artificial Neurons and Synapses Based on Al/a-SiNxOy:H/P+-Si Device with Tunable Resistive Switching from Threshold to Memory

“…To make it possible for the Si-based artificial neurons and synapse to be integrated with neuromorphic chip, the controllable MS and TS characteristic is in high demanded for their perfect compatibility with the mature CMOS technology [ 5 ]. In a hardware neural network, artificial electronic synapses modulate the signal transmission via the synaptic weight update, represented by the device conductance modification [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. To ensure the bioelectronic synapse matrix works efficiently, TS memory is needed to emulate integrate-and-fire function of neurons, which is combined with MS to form the two fundamental elements for hardware neural networks [ 14 , 15 , 16 , 17 ].…”

“…Until now, the material scope of TS memory was mainly limited to oxide-based material such as HfO 2 [ 18 , 21 ], Al 2 O 3 [ 22 ], NiO [ 23 , 24 ], which dependents on the metal conductive pathway. According to the references [ 8 , 9 ], the precise control of ion migration in the resistive switching devices is the performance selection criteria for neuromorphic applications. However, the realization of resistive switching from TS to MS by tuning the Si dangling bond conductive pathway in Si-based RRAM devices is less reported [ 25 , 26 , 27 , 28 , 29 , 30 ].…”