Gunuk Wang

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental s

Abstract The ability of high‐order tuning of the synaptic plasticity in an artificial synapse can offer significant improvement in the processing time, low‐power recognition, and learning capability in a neuro‐inspired computing system. Inspired by light‐assisted dopamine‐facilitated synaptic activity, which achieves rapid learning and adaptation by lowering the threshold of the synaptic plasticity, a two‐terminal organolead halide perovskite (OHP)‐based photonic synapse is fabricated and design

The development of energy-efficient artificial synapses capable of manifoldly tuning synaptic activities can provide a significant breakthrough toward novel neuromorphic computing technology. Here, a new class of artificial synaptic architecture, a three-terminal device consisting of a vertically integrated monolithic tungsten oxide memristor, and a variable-barrier tungsten selenide/graphene Schottky diode, termed as a 'synaptic barrister,' are reported. The device can implement essential synap

A new technology for the fabrication of reliable solid-state molecular devices using a graphene multilayer as the top electrode is introduced. Graphene-electrode molecular devices were fabricated in high yield with good junction conductance. These devices also have excellent durabilities, thermal and operational stabilities, and device lifetimes. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or

One-dimensional (1D) devices are becoming the most desirable format for wearable electronic technology because they can be easily woven into electronic (e-) textile(s) with versatile functional units while maintaining their inherent features under mechanical stress. In this study, we designed 1D fiber-shaped multi-synapses comprising ferroelectric organic transistors fabricated on a 100-μm Ag wire and used them as multisynaptic channels in an e-textile neural network for wearable neuromorphic ap

Ultrathin conformable artificial synapse platforms that can be used as on-body or wearable chips suggest a path to build next-generation, wearable, intelligent electronic systems that can mimic the synaptic operations of the human brain. So far, an artificial synapse architecture with ultimate mechanical flexibility in a freestanding form while maintaining its functionalities with high stability and accuracy on any conformable substrate has not been demonstrated yet. Here, we demonstrate the fir

The human brain intrinsically operates with a large number of synapses, more than 1015. Therefore, one of the most critical requirements for constructing artificial neural networks (ANNs) is to achieve extremely dense synaptic array devices, for which the crossbar architecture containing an artificial synaptic node at each cross is indispensable. However, crossbar arrays suffer from the undesired leakage of signals through neighboring cells, which is a major challenge for implementing ANNs. In t

We studied the molecular configuration-dependent charge transport of alkyl metal-molecule-metal junctions using conducting atomic force microscopy (CAFM). The inflection point (or transition voltage V(T)) on the plot of ln(I/V(2)) versus 1/V shifted to a lower voltage with increasing CAFM tip-loading force and decreasing molecular length. Our results indicate that the reduction of gap distance by molecular tilt configuration enhances the transition of the electronic transport mechanism from dire

Lightweight and flexible tactile learning machines can simultaneously detect, synaptically memorize, and subsequently learn from external stimuli acquired from the skin. This type of technology holds great interest due to its potential applications in emerging wearable and human-interactive artificially intelligent neuromorphic electronics. In this study, an integrated artificially intelligent tactile learning electronic skin (e-skin) based on arrays of ferroelectric-gate field-effect transistor

The effect of metal-molecule contacts in molecular junctions is studied based on the analysis of a statistically significant number of devices and a proposed multibarrier tunneling (MBT) model, where a metal-molecule-metal junction is divided into three individual barriers: a molecular-chain body and metal-molecule contacts on either side of molecule. Using the MBT model with the statistical analysis, we could derive and distinguish decay coefficients for contact barriers $({\ensuremath{\beta}}_

An entire 1-kilobit crossbar device based upon SiOx resistive memories with integrated diodes has been made. The SiOx -based one diode-one resistor device system has promise to satisfy the prerequisite conditions for next generation non-volatile memory applications.

Oxide-based two-terminal resistive random access memory (RRAM) is considered one of the most promising candidates for next-generation nonvolatile memory. We introduce here a new RRAM memory structure employing a nanoporous (NP) silicon oxide (SiOx) material which enables unipolar switching through its internal vertical nanogap. Through the control of the stochastic filament formation at low voltage, the NP SiOx memory exhibited an extremely low electroforming voltage (∼ 1.6 V) and outstanding pe

Abstract Realization of memristor‐based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system‐level. In this sense, uniform and reliable titanium oxide (TiO x ) memristor array devices are fabricated to be utilized as constituent device element in hardware neural network, representing passive matrix array structure enabling vector‐matrix multiplication process between multisignal and trained synaptic weigh

Oxide-based resistive memory systems have high near-term promise for use in nonvolatile memory. Here we introduce a memory system employing a three-dimensional (3D) networked nanoporous (NP) Ta2O5-x structure and graphene for ultrahigh density storage. The devices exhibit a self-embedded highly nonlinear I-V switching behavior with an extremely low leakage current (on the order of pA) and good endurance. Calculations indicated that this memory architecture could be scaled up to a ∼162 Gbit cross