Self-Compliant Threshold Switching Devices with High On/Off ratio by Control of Quantized Conductance in Ag Filaments

Achieving Reliable and Ultrafast Memristors via Artificial Filaments in Silk Fibroin

The practical implementation of memristors in neuromorphic computing and biomimetic sensing suffers from unexpected temporal and spatial variations due to the stochastic formation and rupture of conductive filaments (CFs). Here, the biocompatible silk fibroin (SF) is patterned with an on-demand nanocone array by using thermal scanning probe lithography (t-SPL) to guide and confine the growth of CFs in the silver/SF/gold (Ag/SF/Au) memristor. Benefiting from the high fabrication controllability, cycle-to-cycle (temporal) standard deviation of the set voltage for the structured memristor is significantly reduced by ≈95.5% (from 1.535 to 0.0686 V) and the device-to-device (spatial) standard deviation is also reduced to 0.0648 V. Besides, the statistical relationship between the structural nanocone design and the resultant performance is confirmed, optimizing at the small operation voltage (≈0.5 V) and current (100 nA), ultrafast switching speed (sub-100 ns), large on/off ratio (104 ), and the smallest switching slope (SS < 0.01 mV dec-1 ). Finally, the short-term plasticity and leaky integrated-and-fire behavior are emulated, and a reliable thermal nociceptor system is demonstrated for practical neuromorphic applications.

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.

Self-Assembled Au Nanoelectrodes: Enabling Low-Threshold-Voltage HfO2-Based Artificial Neurons

Filamentary-type resistive switching devices, such as conductive bridge random-access memory and valence change memory, have diverse applications in memory and neuromorphic computing. However, the randomness in filament formation poses challenges to device reliability and uniformity. To overcome this issue, various defect engineering methods have been explored, including doping, metal nanoparticle embedding, and extended defect utilization. In this study, we present a simple and effective approach using self-assembled uniform Au nanoelectrodes to controll filament formation in HfO2 resistive switching devices. By concentrating the electric field near the Au nanoelectrodes within the BaTiO3 matrix, we significantly enhanced the device stability and reduced the threshold voltage by up to 45% in HfO2-based artificial neurons compared to the control devices. The threshold voltage reduction is attributed to the uniformly distributed Au nanoelectrodes in the insulating matrix, as confirmed by COMSOL simulation. Our findings highlight the potential of nanostructure design for precise control of filamentary-type resistive switching devices.