Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing

The von Neumann architecture has faced challenges requiring high-fulfillment levels due to the performance gap between its processor and memory. Among the numerous resistive-switching random-access memories, the properties of hexagonal boron nitride (BN) have been extensively reported, but those of amorphous BN have been insufficiently explored for memory applications. Herein, we fabricated a Pt/BN/TiN device utilizing the resistive switching mechanism to achieve synaptic characteristics in a neuromorphic system. The switching mechanism is investigated based on the I-V curves. Utilizing these characteristics, we optimize the potentiation and depression to mimic the biological synapse. In artificial neural networks, high-recognition rates are achieved using linear conductance updates in a memristor device. The short-term memory characteristics are investigated in depression by controlling the conductance level and time interval.

Voltage-Controlled Conversion from CDS to MDS in an Azobenzene-Based Organic Memristor for Information Storage and Logic Operations

For organic memristors, non-zero-crossing current-voltage (I-V) curves are often observed, which can be attributed to capacitive effects. If the conversion between the capacitance-dominated state (CDS) and the memristance-dominated state (MDS) can be realized in a controllable manner, more device functions can be obtained. In this work, a two-terminal memristor using a common organic dye, azobenzene (AZB), as the active layer was prepared. It is found that as the applied voltage gradually increases, the device can transition from CDS to MDS. In the low voltage range (<1 V), the device is in CDS, and the capacitance is significantly increased by ∼10⁴ compared to the theoretical value. In the high voltage range (>1 V), the device is in MDS, achieving an HRS (high resistance state)/LRS (low resistance state) resistance ratio of ∼10⁴, and the logic operations are achieved. Through the analysis of the I-V curve, energy diagram of the materials, and computer simulation results, the mechanisms of CDS, MDS, and their conversion are proposed. This work provides an in-depth understanding of the working mechanism of organic memristors and demonstrates the potential of AZB-based organic memristors for information storage and logic display applications.

Dynamic Model of the Short-Term Synaptic Behaviors of PEDOT-based Organic Electrochemical Transistors with Modified Shockley Equations

Neuromorphic computing is an emerging area with prospects to break the energy efficiency bottleneck of artificial intelligence (AI). A crucial challenge for neuromorphic computing is understanding the working principles of artificial synaptic devices. As an emerging class of synaptic devices, organic electrochemical transistors (OECTs) have attracted significant interest due to ultralow voltage operation, analog conductance tuning, mechanical flexibility, and biocompatibility. However, little work has been focused on the first-principal modeling of the synaptic behaviors of OECTs. The simulation of OECT synaptic behaviors is of great importance to understanding the OECT working principles as neuromorphic devices and optimizing ultralow power consumption neuromorphic computing devices. Here, we develop a two-dimensional transient drift-diffusion model based on modified Shockley equations for poly(3,4-ethylenedioxythiophene) (PEDOT)-based OECTs. We reproduced the typical transistor characteristics of these OECTs including the unique non-monotonic transconductance-gate bias curve and frequency dependency of transconductance. Furthermore, typical synaptic phenomena, such as excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and short-term plasticity (STP), are also demonstrated. This work is crucial in guiding the experimental exploration of neuromorphic computing devices and has the potential to serve as a platform for future OECT device simulation based on a wide range of semiconducting materials.

Improved resistive switching characteristics of a multi-stacked HfO2/Al2O3/HfO2 RRAM structure for neuromorphic and synaptic applications: experimental and computational study

Atomic Layer Deposition (ALD) was used for a tri-layer structure (HfO2/Al2O3/HfO2) at low temperature over an Indium Tin Oxide (ITO) transparent electrode. First, the microstructure of the fabricated TaN/HfO2/Al2O3/HfO2/ITO RRAM device was examined by the cross-sectional High-Resolution Transmission Electron Microscopy (HRTEM). Then, Energy Dispersive X-ray Spectroscopy (EDS) was performed to probe compositional mapping. The bipolar resistive switching mode of the device was confirmed through SET/RESET characteristic plots for 100 cycles as a function of applied biasing voltage. An endurance test was performed for 100 DC switching cycles @0.2 V wherein; data retention was found up to 104 s. Moreover, for better insight into the charge conduction mechanism in tri-layer HfO2/Al2O3/HfO2, based on oxygen vacancies (VOX), total density of states (TDOS), partial density of states (PDOS) and isosurface three-dimensional charge density analysis was performed using WEIN2k and VASP simulation packages under Perdew-Burke-Ernzerhof _Generalized Gradient approximation (PBE-GGA). The experimental and theoretical outcomes can help in finding proper stacking of the active resistive switching (RS) layer for resistive random-access memory (RRAM) applications.

ABO3 multiferroic perovskite materials for memristive memory and neuromorphic computing

The unique electron spin, transfer, polarization and magnetoelectric coupling characteristics of ABO₃ multiferroic perovskite materials make them promising candidates for application in multifunctional nanoelectronic devices. Reversible ferroelectric polarization, controllable defect concentration and domain wall movement originated from the ABO₃ multiferroic perovskite materials promotes its memristive effect, which further highlights data storage, information processing and neuromorphic computing in diverse artificial intelligence applications. In particular, ion doping, electrode selection, and interface modulation have been demonstrated in ABO₃-based memristive devices for ultrahigh data storage, ultrafast information processing, and efficient neuromorphic computing. These approaches presented today including controlling the dopant in the active layer, altering the oxygen vacancy distribution, modulating the diffusion depth of ions, and constructing the interface-dependent band structure were believed to be efficient methods for obtaining unique resistive switching (RS) behavior for various applications. In this review, internal physical dynamics, preparation technologies, and modulation methods are systemically examined as well as the progress, challenges, and possible solutions are proposed for next generation emerging ABO₃-based memristive application in artificial intelligence.

Humidity Effect on Resistive Switching Characteristics of the CH3NH3PbI3 Memristor

Organic-inorganic hybrid halide perovskites (OIHPs) with inherent mixed ionic-electronic conduction ability have been proposed as promising candidates for memristors with unique optoelectronic characteristics. Despite the great achievements toward understanding the working mechanism and exploring their functionality as water-sensitive materials, the humidity effect on the resistive switching (RS) characteristics still remains to be studied. This study investigates the humidity effect on the RS characteristics of Au/CH₃NH₃PbI₃/FTO memristor. The memristor works well at moderate relative humidity (RH, <75%) and degrades rapidly at higher RH of 90%. An obvious decrease in low resistance states on increasing the RH level is observed, which could be attributed to water-induced reduction of the iodide migration barrier. Raman and X-ray diffraction analyses indicate that the migration barrier reduction possibly originated from the weakening of the Pb-I bond caused by the intercalation of water molecules into the crystal lattice. The humidity-sensitive RS characteristics of the memristor could extend the scope of OIHP application for sensing and security applications and also prompt researchers to pay attention to the humidity effect on memristor devices with OIHPs.

Refining the Negative Differential Resistance Effect in a TiOx-Based Memristor

The N-type negative difference resistance (NDR) is characterized by the peak/valley voltage (V_p/V_v) and the corresponding current (I_p/I_v). The N-type NDR is observed in the resistive switching (RS) memory device of Ag|TiO₂|F-doped SnO₂ at room temperature. After the TiO₂ film is equipped with a nanoporous array, the ∼1.2 V gap voltage between V_p and V_v is effectively downscaled to ∼0.5 V, and the gap current of ∼7.23 mA between I_p and I_v is improved to ∼30 mA. It demonstrates that a lower power consumption and faster switching time of the NDR can be obtained in the memristor. Compensations and synergies among the nanoscale conduction filaments (OH^-, Ag⁺, and V_o) are responsible for the refining NDR behavior in our devices. This work provides an efficient method to construct a high-performance N-type NDR effect at room temperature and gives a new horizon on the coexistence of this type of NDR effect and RS memory behaviors.

Recent Process of Flexible Transistor-Structured Memory

Flexible transistor-structured memory (FTSM) has attracted great attention for its important role in flexible electronics. For nonvolatile information storage, FTSMs with floating-gate, charge-trap, and ferroelectric mechanisms have been developed. By introducing an optical sensory module, FTSM can be operated by optical inputs to function as an optical memory transistor. As a special type of FTSM, transistor-structured artificial synapse emulates important functions of a biological synapse to mimic brain-inspired memory behaviors and nervous signal transmissions. This work reviews the recent development of the above mentioned FTSMs, with a focus on working mechanism and materials, and flexibility.

Reversible Transformation between Bipolar Memory Switching and Bidirectional Threshold Switching in 2D Layered K-Birnessite Nanosheets

Birnessite-related manganese dioxides (MnO₂) have recently been studied owing to their diverse low-dimensional layered structures and potential applications in energy devices. The birnessite MnO₂ possesses a layered structure with edge-shared MnO₆ octahedra layer stacked with interlayer of cations. The unique layered structure may provide some distinct electrical properties for the 2D layered nanosheets. In this work, layered K-birnessite MnO₂ samples are synthesized by a hydrothermal method. The resistive switching (RS) devices based on single K-birnessite MnO₂ nanosheets are fabricated by transferring the nanosheets onto SiO₂/Si substrates through a facile and feasible method of mechanical exfoliation. The device exhibits nonvolatile memory switching (MS) behaviors with high current ON/OFF ratio of ∼2 × 10⁵. And more importantly, reversible transformation between the nonvolatile MS and volatile threshold switching (TS) can be achieved in the single layered nanosheet through tuning the magnitude of compliance current (I_cc). To be more specific, a relatively high I_cc (1 mA) can trigger the nonvolatile MS behaviors, while a relatively low I_cc (≤100 μA) can generate volatile TS characteristics. This work not only demonstrates the memristor based on single birnessite-related MnO₂ nanosheet, but also offers an insight into understanding the complex resistive switching types and relevant physical mechanisms of the 2D layered oxide nanosheets.

Multilevel resistive switching memory behaviors arising from ion diffusion and photoelectron transfer in α-Fe2O3 nano-island arrays

Resistive switching (RS) memory behaviors are observed in an Ag|α-Fe₂O₃|Ti device after operating under an ultralow bias voltage of ±0.1 V. An SET voltage of ∼20 mV is obtained under illumination. Multilevel RS memory is realized under photoelectric signal control. The separation and fast transfer of hole-electron pairs are responsible for the enhanced RS memory under illumination.

Logic Gate Functions Built with Nonvolatile Resistive Switching and Thermoresponsive Memory Based on Biologic Proteins

Logic gate functions built with nonvolatile resistive switching and thermoresponsive memory based on biologic proteins were investigated. The "NAND" and "NOR" functions of logic gates in soya protein devices have been built at room temperature by their nonvolatile ternary WORM resistive switching behaviors. Furthermore, heating the devices from room temperature to 358 K results in a switch from tristable state to bistable state WORM resistive switching behavior, indicating that the thermoresponsiveness can be efficiently memorized. The biologic transient nonvolatile memory device consisting of soya protein is illustrated. This device exhibits a long data retention time (104 s) and significant HRS/LRS ratio (∼105); the transient response of the current to voltage of an as-fabricated device is also explored. The soya protein based memory device on a gelatin film substrate is also assessed to validate the feasibility of degradation and biological compatibility for the implantable biological electronic device, that is, innoxious and avirulent to the human body. This can offer alternative avenues for exploring prospective bioelectronic devices.

Evolution map of the memristor: from pure capacitive state to resistive switching state

Memristors possess great application prospects in terabit nonvolatile storage devices, memory-in-logic algorithmic chips and bio-inspired artificial neural network systems. However, "what is the origin state of the memristor?" has remained an unanswered question for half a century. While many applications rely on the memristor, its origin state is becoming a fundamental issue. Herein, we reveal a new state, the pure capacitance state (PCS), which occurs before the memristor is triggered, and the origin state of the memristor can be verified in the memory cells through controlling the ambience parameters. Discovery of the PCS, a missing earlier stage of the memristor, completes the whole evolution map of the memristor from the very beginning to the final developed state.