Transparent and Flexible Inorganic Perovskite Photonic Artificial Synapses with Dual-Mode Operation

With the rapid development of artificial intelligence, the simulation of the human brain for neuromorphic computing has demonstrated unprecedented progress. Photonic artificial synapses are strongly desirable owing to their higher neuron selectivity, lower crosstalk, wavelength multiplexing capabilities, and low operating power compared to their electric counterparts. This study demonstrates a highly transparent and flexible artificial synapse with a two-terminal architecture that emulates photonic synaptic functionalities. This optically triggered artificial synapse exhibits clear synaptic characteristics such as paired-pulse facilitation, short/long-term memory, and synaptic behavior analogous to that of the iris in the human eye. Ultraviolet light illumination-induced neuromorphic characteristics exhibited by the synapse are attributed to carrier trapping and detrapping in the SnO₂ nanoparticles and CsPbCl₃ perovskite interface. Moreover, the ability to detect deep red light without changes in synaptic behavior indicates the potential for dual-mode operation. This study establishes a novel two-terminal architecture for highly transparent and flexible photonic artificial synapse that can help facilitate higher integration density of transparent 3D stacking memristors, and make it possible to approach optical learning, memory, computing, and visual recognition.     

Fabrication and investigation of ferroelectric memristors with various synaptic plasticities

In the post-Moore era, neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks. Memristors have been proposed as a key part of neuromorphic computing architectures, and can be used to emulate the synaptic plasticities of the human brain. Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage, low write/read latency and tunable conductive states. However, among the reported ferroelectric memristors, the mechanisms of resistive switching are still under debate. In addition, there needs to be more research on emulation of the brain synapses using ferroelectric memristors. Herein, Cu/PbZr_0.52Ti_0.48O₃ (PZT)/Pt ferroelectric memristors have been fabricated. The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior. The synaptic plasticities, including excitatory post-synaptic current, paired-pulse facilitation, paired-pulse depression and spike time-dependent plasticity, have been mimicked by the PZT devices. Furthermore, the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models. This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.     

Synaptic Suppression Triplet‐STDP Learning Rule Realized in Second‐Order Memristors

The synaptic weight modification depends not only on interval of the pre‐/postspike pairs according to spike‐timing dependent plasticity (classical pair‐STDP), but also on the timing of the preceding spike (triplet‐STDP). Triplet‐STDP reflects the unavoidable interaction of spike pairs in natural spike trains through the short‐term suppression effect of preceding spikes. Second‐order memristors with one state variable possessing short‐term dynamics work in a way similar to the biological system. In this work, the suppression triplet‐STDP learning rule is faithfully demonstrated by experiments and simulations using second‐order memristors. Furthermore, a leaky‐integrate‐and‐fire (LIF) neuron is simulated using a circuit constructed with second‐order memristors. Taking the advantage of the LIF neuron, various neuromimetic dynamic processes, including local graded potential leaking out, postsynaptic impulse generation and backpropagation, and synaptic weight modification according to the suppression triplet‐STDP rule, are realized. The realized weight‐dependent pair‐ and triplet‐STDP rules are clearly in line with findings in biology. The physically realized triplet‐STDP rule is powerful in developing direction and speed selectivity for complex pattern recognition and tracking tasks. These scalable artificial synapses and neurons realized in second‐order memristors can intrinsically capture the neuromimetic dynamic processes; they are the promising building blocks for constructing brain‐inspired computation systems.     

Memristor with Ag‐Cluster‐Doped TiO2 Films as Artificial Synapse for Neuroinspired Computing

Memristor, based on the principle of biological synapse, is recognized as one of the key devices in confronting the bottleneck of classical von Neumann computers. However, conventional memristors are difficult to continuously adjust the conduction and dutifully mimic the biosynapse function. Here, TiO₂ films with self‐assembled Ag nanoclusters implemented by gradient Ag dopant are employed to achieve enhanced memristor performance. The memristors exhibit gradual both potentiating and depressing conduction under positive and negative pulse trains, which can fully emulate excitation and inhibition of biosynapse. Moreover, comprehensive biosynaptic functions and plasticity, including the transition from short‐term memory to long‐term memory, long‐term potentiation and depression, spike‐timing‐dependent plasticity, and paired‐pulse facilitation, are implemented with the fabricated memristors in this work. The applied pulses with a width of hundreds of nanoseconds timescale are beneficial to realize fast learning and computing. High‐resolution transmission electron microscopy observations clearly demonstrate that Ag clusters redistribute to form Ag conductive filaments between Ag and Pt electrode under electrical field at ON‐state device. The experimental data confirm that the oxides doped with Ag clusters have the potential for mimicking biosynaptic behavior, which is essential for the further creation of artificial neural systems.     

Optically Modulated Threshold Switching in Core–Shell Quantum Dot Based Memristive Device

The threshold switching (TS) phenomenon in memristors has drawn great attention for its versatile applications in selectors, artificial neurons, true random number generators, and electronic integrations. The transition between nonvolatile resistive switching and volatile TS modes can be realized by doping, varying annealing and voltage sweeping conditions, or imposing different compliance current. Here, a strategy is reported to achieve such transition by the noninvasive UV light stimulus based on InP/ZnS quantum dot (QD) memristor. The core–shell InP/ZnS QDs with quasi‐type II band alignment ensures photoexcited electrons localized in InP core, photoexcited hole state distributed in the outer shell, and subsequent lifetime controlling of conductive filament under light irradiation. Systematic mechanism investigations indicate that UV photogenerated holes are accumulated on the surface of the QD film, which is consistent with rapid transfer of photogenerated holes in the core–shell InP/ZnS structure. Based on the light‐modulated effect, a reconfigurable 9 × 9 visual data storage array with a key pattern and a simple leaky integrate‐and‐fire circuit are constructed. These results suggest the potential of direct optical modulation of memory mode through energy band engineering, leading to future optoelectronic and electronic device for the implementation of neuromorphic visual system and artificial neural networks.     

Photoassisted Electroforming Method for Reliable Low‐Power Organic–Inorganic Perovskite Memristors

Organic–inorganic hybrid perovskite memristors with high resistive‐switching (RS) reliability and low power consumption are crucial for high‐density storage and high‐efficiency neuromorphic computing. However, the current overshoot in the electroforming process generally induces overgrowth of conductive filaments (CFs) and degrades the RS performance. Here, a simple photo‐assisted electroforming (PAE) method to suppress the current overshoot, in which the visible light irradiation is introduced into the initial electroforming process, is proposed for the first time. As a result, a reliable memristor with reduced RS fluctuation and enhanced cycling endurance is obtained, and also, the low operating current of 0.06 mA and low powerconsumption of 0.12 mW are achieved, which are about one order of magnitude lower than those of most reported hybrid perovskite‐based memristors. Further experimental evidence indicates that light irradiation plays dual roles: 1) the light‐induced lowering of iodide migration barrier leads to a significant reduction of overshoot current and forming voltage; 2) the enhanced local photoconductivity of the perovskite film shares the overshoot current through the CFs. Both factors limit the total quantity of vacancy defects generated in the electroforming process, thus preventing undesirable overgrowth of the CFs. The present PAE strategy has promise for developing high‐performance memristors.     

Control of Switching Modes and Conductance Quantization in Oxygen Engineered HfOx based Memristive Devices

Hafnium oxide (HfO_x)‐based memristive devices have tremendous potential as nonvolatile resistive random access memory (RRAM) and in neuromorphic electronics. Despite its seemingly simple two‐terminal structure, a myriad of RRAM devices reported in the rapidly growing literature exhibit rather complex resistive switching behaviors. Using Pt/HfO_x/TiN‐based metal–insulator–metal structures as model systems, it is shown that a well‐controlled oxygen stoichiometry governs the filament formation and the occurrence of multiple switching modes. The oxygen vacancy concentration is found to be the key factor in manipulating the balance between electric field and Joule heating during formation, rupture (reset), and reformation (set) of the conductive filaments in the dielectric. In addition, the engineering of oxygen vacancies stabilizes atomic size filament constrictions exhibiting integer and half‐integer conductance quantization at room temperature during set and reset. Identifying the materials conditions of different switching modes and conductance quantization contributes to a unified switching model correlating structural and functional properties of RRAM materials. The possibility to engineer the oxygen stoichiometry in HfO_x will allow creating quantum point contacts with multiple conductance quanta as a first step toward multilevel memristive quantum devices.