Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems.

Reservoir computing (RC) system is based upon the reservoir layer, which non-linearly transforms input signals into high-dimensional states, facilitating simple training in the readout layer-a linear neural network. These layers require different types of devices-the former demonstrated as diffusive memristors and the latter prepared as drift memristors. The integration of these components can increase the structural complexity of RC system. Here, a reconfigurable resistive switching memory (RSM) capable of implementing both diffusive and drift dynamics is demonstrated. This reconfigurability is achieved by preparing a medium with a 3D ion transport channel (ITC), enabling precise control of the metal filament that determines memristor operation. The 3D ITC-RSM operates in a volatile threshold switching (TS) mode under a weak electric field and exhibits short-term dynamics that are confirmed to be applicable as reservoir elements in RC systems. Meanwhile, the 3D ITC-RSM operates in a non-volatile bipolar switching (BS) mode under a strong electric field, and the conductance modulation metrics forming the basis of synaptic weight update are validated, which can be utilized as readout elements in the readout layer. Finally, an RC system is designed for the application of reconfigurable 3D ITC-RSM, and performs real-time recognition on Morse code datasets.

One-Shot Remote Integration of Macromolecular Synaptic Elements on a Chip for Ultrathin Flexible Neural Network System.

The field of biomimetic electronics that mimic synaptic functions has expanded significantly to overcome the limitations of the von Neumann bottleneck. However, the scaling down of the technology has led to an increasingly intricate manufacturing process. To address the issue, this work presents a one-shot integrable electropolymerization (OSIEP) method with remote controllability for the deposition of synaptic elements on a chip by exploiting bipolar electrochemistry. Condensing synthesis, deposition, and patterning into a single fabrication step is achieved by combining alternating-current voltage superimposed on direct-current voltage-bipolar electropolymerization and a specially designed dual source/drain bipolar electrodes. As a result, uniform 6 × 5 arrays of poly(3,4-ethylenedioxythiophene) channels are successfully fabricated on flexible ultrathin parylene substrates in one-shot process. The channels exhibited highly uniform characteristics and are directly used as electrochemical synaptic transistor with synaptic plasticity over 100 s. The synaptic transistors have demonstrated promising performance in an artificial neural network (NN) simulation, achieving a high recognition accuracy of 95.20%. Additionally, the array of synaptic transistor is easily reconfigured to a multi-gate synaptic circuit to implement the principles of operant conditioning. These results provide a compelling fabrication strategy for realizing cost-effective and disposable NN systems with high integration density.

Highly Reliable 3D Channel Memory and Its Application in a Neuromorphic Sensory System for Hand Gesture Recognition.

Brain-inspired neuromorphic computing systems, based on a crossbar array of two-terminal multilevel resistive random-access memory (RRAM), have attracted attention as promising technologies for processing large amounts of unstructured data. However, the low reliability and inferior conductance tunability of RRAM, caused by uncontrollable metal filament formation in the uneven switching medium, result in lower accuracy compared to the software neural network (SW-NN). In this work, we present a highly reliable CoOx-based multilevel RRAM with an optimized crystal size and density in the switching medium, providing a three-dimensional (3D) grain boundary (GB) network. This design enhances the reliability of the RRAM by improving the cycle-to-cycle endurance and device-to-device stability of the I-V characteristics with minimal variation. Furthermore, the designed 3D GB-channel RRAM (3D GB-RRAM) exhibits excellent conductance tunability, demonstrating high symmetricity (624), low nonlinearity (ßLTP/ßLTD ∼ 0.20/0.39), and a large dynamic range (Gmax/Gmin ∼ 31.1). The cyclic stability of long-term potentiation and depression also exceeds 100 cycles (105 voltage pulses), and the relative standard deviation of Gmax/Gmin is only 2.9%. Leveraging these superior reliability and performance attributes, we propose a neuromorphic sensory system for finger motion tracking and hand gesture recognition as a potential elemental technology for the metaverse. This system consists of a stretchable double-layered photoacoustic strain sensor and a crossbar array neural network. We perform training and recognition tasks on ultrasonic patterns associated with finger motion and hand gestures, attaining a recognition accuracy of 97.9% and 97.4%, comparable to that of SW-NN (99.8% and 98.7%).