Electroforming in Metal-Oxide Memristive Synapses

Abstract

Memristors have shown an extraordinary potential to emulate the plastic and dynamic electrical behaviors of biological synapses and have been already used to construct neuromorphic systems with in-memory computing and unsupervised learning capabilities; moreover, the small size and simple fabrication process of memristors make them ideal candidates for ultradense configurations. So far, the properties of memristive electronic synapses (i.e., potentiation/depression, relaxation, linearity) have been extensively analyzed by several groups. However, the dynamics of electroforming in memristive devices, which defines the position, size, shape, and chemical composition of the conductive nanofilaments across the device, has not been analyzed in depth. By applying ramped voltage stress (RVS), constant voltage stress (CVS), and pulsed voltage stress (PVS), we found that electroforming is highly affected by the biasing methods applied. We also found that the technique used to deposit the oxide, the chemical composition of the adjacent metal electrodes, and the polarity of the electrical stimuli applied have important effects on the dynamics of the electroforming process and in subsequent post-electroforming bipolar resistive switching. This work should be of interest to designers of memristive neuromorphic systems and could open the door for the implementation of new bioinspired functionalities into memristive neuromorphic systems.

Random Telegraph Noise in Metal‐Oxide Memristors for True Random Number Generators: A Materials Study

“…[ 13 ] Atomic defects enabling the observation of RTN can be formed due to the intrinsic variability of the manufacturing processes at the atomic scale (e.g., heavy‐ion implantation, surface contamination) [ 14 ] or after an electrical stress is applied (e.g., electrical‐field‐driven ionic movement). [ 15,16 ]…”

Resistive switching in FTO/CuO–Cu 2 O/Au memory devices

“…Before performing I – V measurements, the electroforming process is required to create a sufficiently strong electric field and initiate a soft breakdown of the switching layer in the device. As a result, massive oxygen vacancies are introduced into the copper oxide layer, which can form nanoscale conductive filaments [26]. To this aim, the applied voltage is slowly increased with a current compliance of 10 mA to protect the device from a permanent breakdown (not shown here).…”

Memristive devices based hardware for unlabeled data processing

“…Follow the same principle, SNN using STDP learning based on memristive devices have been realized by many other materials, such as organic FTJ (P(VDF-TrFE)) [132], metal oxide (HfO 2 [133][134][135][136][137][138], TiO x [139], Al 2 O 3 [140], Nb x O y [28], and (CoFeB) x (LiNbO3) 1−x [118]), and perovskite semiconductors (MAPbBr 3 , FAPbBr 3 , CsPbBr 3 [141]).…”

“…The key parameters for forming processes are the voltage for electroforming, the electroforming time, and the presence and quality of switching behavior after electroforming. These parameters strongly depend on the insulating material as well as the deposit method [140]. Electroforming-free has been achieved in memristive tunneling junctions [162] and TaO x -based memristor [67], but still needs to be developed in many other memristive devices.…”