Resistive Switching Memristor: On the Direct Observation of Physical Nature of Parameter Variability

Abstract

Ion-based memristive switching has attracted widespread attention from industries owing to its outstanding advantages in storage and neuromorphic computing. Major issues for achieving brain-inspired computation of highly functional memory in redox-based ion devices are relatively large variability in their operating parameters and limited cycling endurance. In some devices, volatile and nonvolatile operations often replace each other without changing operating conditions. To address these issues, it is important to observe directly what is happening in repeated operations. Herein, we use a planar device that enables direct capturing of microscopic behaviors in the nucleation and growth of metal whiskers under repeated switching to verify the microscopic origin of the large parameter variability. We report direct observations that reveal the physical origin for the large cycle-to-cycle and device-to-device variability in memristive switching, which was achieved using planar polymer atomic switches with a gap >1 μm. We find that the deposition location of metal atoms is closely related to the crystallinity of the ion transport layer (solid polymer electrolyte, SPE). The filament variability (shape, position, quantity, etc.) during different cycles and devices is indeed the main reason for the observed variability in the operating characteristics. The results shed unique light on the complexity of the operation of the ion device, that is, the evolution of the dielectric layer and metal filament must be considered.

Reliable Memristor Based on Ultrathin Native Silicon Oxide

“…In contrast, in the case of t = 2.5 nm, which is close to the thickness of our ultrathin SiO x , multiple fragile filaments form between the electrodes because of the shortened ion transport path (Figure b). Such multiple filaments might lead to large switching variability and poor retention. , Fortunately, our simulation shows that a single robust filament can still be formed in the 2.5 nm thick electrolyte with a larger protrusion of r = 1.5 nm (Figure c), because of the greatly enhanced electric field near to the large protrusion (Figure S4a). More importantly, the randomness of the CF is also depressed by the localized electric field, contributing to reliable RS performance. , …”

“…The filament-type memristor (FM) is known as one of the most promising technologies for constructing neuromorphic computing systems, − and its intrinsic randomness can be exploited for security hardware such as true random number generators (TRNGs). − However, most FMs exhibit unreliable resistive switching (RS) with device parameters (i.e., set/reset voltages, high/low resistance states) drifting during cycle-to-cycle (C2C) operations, − which severely decreases the learning accuracy of neural networks and damages the probability distribution as required for a TRNG. In response to the challenge, there is a fast-growing interest in developing FM based on 2D nanomaterials (thickness typically <5 nm). − Such a device can exhibit ultralow energy consumption and improved C2C uniformity, because the conducting filaments (CF) can be strictly constricted in atomic-scale regions. − Unfortunately, because of the poor control of local defects (e.g., holes, cracks, folds, wrinkles, and grain boundaries) of polycrystalline 2D layered materials, those devices suffer from unfavorable leakage and inhomogeneous charge conduction, resulting in low production yields and large device-to-device (D2D) variability. − Furthermore, the general performance of memristors based on 2D nanomaterials is not superior to that of traditional memristors based on thick electrolytes, potentially because of the formation of large amounts of CFs, which would induce significant filament instability. , In addition, these atomically thin materials and associated fabrication processes are not fully compatible with the complementary metal-oxide-semiconductor (CMOS) technology, hindering implementation in practical circuits.…”

Enhancing memristor fundamentals through instrumental characterization and understanding reliability issues

“…This significant switching variation is mainly due to the formation of multiple delicate conductive filaments. 173,174 After a few switching cycles, the abrupt switching behavior of the device becomes uniform analog switching, as shown with black lines in Fig. 9e.…”

Contrasting analog and digital resistive switching memory characteristics in solution-processed copper(i) thiocyanate and its polymer electrolyte-based memristive devices

“…These studies confirm the switching mechanism to be ECM or conductive-bridge random access memory (CBRAM) type, as observed in other polymer-electrolyte systems with electrochemically active electrodes. 58,59 The formation and dissolution of the copper filament are responsible for the ON and OFF states, and are controlled by the electrochemical redox reaction at the interfaces.…”