Unraveling the origins of the coexisting localized-interfacial mechanism in oxide-based memristors in CMOS-integrated synaptic device implementations.

The forefront of neuromorphic research strives to develop devices with specific properties, i.e., linear and symmetrical conductance changes under external stimuli. This is paramount for neural network accuracy when emulating a biological synapse. A parallel exploration of resistive memory as a replacement for conventional computing memory ensues. In search of a holistic solution, the proposed memristive device in this work is uniquely poised to address this elusive gap as a unified memory solution. Opposite biasing operations are leveraged to achieve stable abrupt and gradual switching characteristics within a single device, addressing the demands for lower latency and energy consumption for binary switching applications, and graduality for neuromorphic computing applications. We evaluated the underlying principles of both switching modes, attributing the anomalous gradual switching to the modulation of oxygen-deficient layers formed between the active electrode and oxide switching layer. The memristive cell (1R) was integrated with 40 nm transistor technology (1T) to form a 1T-1R memory cell, demonstrating a switching speed of 50 ns with a pulse amplitude of ±2.5 V in its forward-biased mode. Applying pulse trains of 20 ns to 490 ns in the reverse-biased mode exhibited synaptic weight properties, obtaining a nonlinearity (NL) factor of <0.5 for both potentiation and depression. The devices in both modes also demonstrated an endurance of >106 cycles, and their conductance states were also stable under temperature stress at 85 °C for 104 s. With the duality of the two switching modes, our device can be used for both memory and synaptic weight-storing applications.

High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing.

Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.

A bi-functional three-terminal memristor applicable as an artificial synapse and neuron.

Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors face asynchronous programming/reading operation issues. Here, a three-terminal memristor (3TM) based on oxygen ion migration is developed to function as both a synapse and a neuron. We demonstrate short-term plasticity such as pair-pulse facilitation and high-pass dynamic filtering in our devices. Additionally, a 'learning-forgetting-relearning' behavior is successfully mimicked, with lower power required for the relearning process than the first learning. Furthermore, by leveraging the short-term dynamics, the leaky-integrate-and-fire neuronal model is emulated by the 3TM without adopting an external capacitor to obtain the leakage property. The proposed bi-functional 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of an SNN.

Strain-induced degradation and recovery of flexible NbOx-based threshold switching device.

We investigate the functionality of NbOx-based selector devices on a flexible substrate. It was observed that the failure mechanism of cyclic tensile strain is from the disruption of atom arrangements, which essentially led to the crack formation of the film. When under cyclic compressive strain, buckling delamination of the film occurs as the compressed films have debonded from their neighboring layers. By implementing an annealing process after the strain-induced degradation, recovery of the device is observed with reduced threshold and hold voltages. The physical mechanism of the device is investigated through Poole-Frenkel mechanism fitting, which provides insights into the switching behavior after mechanical strain and annealing process. The result demonstrates the potential of the NbOx device in flexible electronics applications with a high endurance of up to 105 cycles of cyclic bending strain and the recovery of the device after degradation.

Proton-Assisted Redox-Based Three-Terminal Memristor for Synaptic Device Applications.

Emerging technologies, i.e., spintronics, 2D materials, and memristive devices, have been widely investigated as the building block of neuromorphic computing systems. Three-terminal memristor (3TM) is specifically designed to mitigate the challenges encountered by its two-terminal counterpart as it can concurrently execute signal transmission and memory operations. In this work, we present a complementary metal-oxide-semiconductor-compatible 3TM with highly linear weight update characteristics and a dynamic range of ∼15. The switching mechanism is governed by the migration of oxygen ions and protons in and out of the channel under an external gate electric field. The involvement of the protonic defects in the electrochemical reactions is proposed based on the bipolar pulse trains required to initiate the oxidation process and the device electrical characteristics under different humidity levels. For the synaptic operation, an excellent endurance performance with over 256k synaptic weight updates was demonstrated while maintaining a stable dynamic range. Additionally, the synaptic performance of the 3TM is simulated and implemented into a four-layer neural network (NN) model, achieving an accuracy of ∼92% in MNIST handwritten digit recognition. With such desirable conductance modulation characteristics, our proposed 3T-memristor is a promising synaptic device candidate to realize the hardware implementation of the artificial NN.

Enhancement of temperature-modulated NbO2-based relaxation oscillator via interfacial and bulk treatments.

This work demonstrates oscillation frequency modulation in a NbO2-based relaxation oscillator device, in which the oscillation frequency increases with operating temperature and source voltage, and decreases with load resistance. An annealing-induced oxygen diffusion at 373 K was carried out to optimize the stoichiometry of the bulk NbO2to achieve consistent oscillation frequency shift with device temperature. The device exhibits stable self-sustained oscillation in which the frequency can be modulated between 2 and 33 MHz, and a wider operating voltage range can be obtained. An additional surface treatment step was employed during fabrication to reduce the surface roughness of the bottom electrode and to remove surface contaminants that affect the interfacial properties of the device. The device frequency tunability coupled with high oscillating frequency and high endurance capability of more than 1.5 × 108cycles indicates that the Pt/NbO2/Pt device is particularly suitable for applications in an oscillatory neural network.

Frequency-Dependent Synapse Weight Tuning in 1S1R with a Short-Term Plasticity TiOx-Based Exponential Selector.

Short-term plasticity (STP) is an important synaptic characteristic in the hardware implementation of artificial neural networks (ANN), as it enables the temporal information processing (TIP) capability. However, the STP feature is rather challenging to reproduce from a single nonvolatile resistive random-access memory (RRAM) element, as it requires a certain degree of volatility. In this work, a Pt/TiOx/Pt exponential selector is introduced not only to suppress the sneak current but also to enable the TIP feature in a one selector-one RRAM (1S1R) synaptic device. Our measurements reveal that the exponential selector exhibits the STP characteristic, while a Pt/HfOx/Ti RRAM enables the long-term memory capability of the synapse. Thereafter, we experimentally demonstrated pulse frequency-dependent multilevel switching in the 1S1R device, exhibiting the TIP capability of the developed 1S1R synapse. The observed STP of the selector is strongly influenced by the bottom metal-oxide interface, in which Ar plasma treatment on the bottom Pt electrode resulted in the annihilation of the STP feature in the selector. A mechanism is thus proposed to explain the observed STP, using the local electric field enhancement induced at the metal-oxide interface coupled with the drift-diffusion model of mobile O2- and Ti3+ ions. This work therefore provides a reliable means of producing the STP feature in a 1S1R device, which demonstrates the TIP capability sought after in hardware-based ANN.

Operando Direct Observation of Filament Formation in Resistive Switching Devices Enabled by a Topological Transformation Molecule.

Conductive filaments (CFs) play a critical role in the mechanism of resistive random-access memory (ReRAM) devices. However, in situ detection and visualization of the precise location of CFs are still key challenges. We demonstrate for the first time the use of a π-conjugated molecule which can transform between its twisted and planar states upon localized Joule heating generated within filament regions, thus reflecting the locations of the underlying CFs. Customized patterns of CFs were induced and observed by the π-conjugated molecule layer, which confirmed the hypothesis. Additionally, statistical studies on filaments distribution were conducted to study the effect of device sizes and bottom electrode heights, which serves to enhance the understanding of switching behavior and their variability at device level. Therefore, this approach has great potential in aiding the development of ReRAM technology.