Scaling Effect on Silicon Nitride Memristor with Highly Doped Si Substrate.

A feasible approach is reported to reduce the switching current and increase the nonlinearity in a complementary metal-oxide-semiconductor (CMOS)-compatible Ti/SiNx /p+ -Si memristor by simply reducing the cell size down to sub-100 nm. Even though the switching voltages gradually increase with decreasing device size, the reset current is reduced because of the reduced current overshoot effect. The scaled devices (sub-100 nm) exhibit gradual reset switching driven by the electric field, whereas that of the large devices (≥1 µm) is driven by Joule heating. For the scaled cell (60 nm), the current levels are tunable by adjusting the reset stop voltage for multilevel cells. It is revealed that the nonlinearity in the low-resistance state is attributed to Fowler-Nordheim tunneling dominating in the high-voltage regime (≥1 V) for the scaled cells. The experimental findings demonstrate that the scaled metal-nitride-silicon memristor device paves the way to realize CMOS-compatible high-density crosspoint array applications.

Nano-cone resistive memory for ultralow power operation.

SiN x -based nano-structure resistive memory is fabricated by fully silicon CMOS compatible process integration including particularly designed anisotropic etching for the construction of a nano-cone silicon bottom electrode (BE). Bipolar resistive switching characteristics have significantly reduced switching current and voltage and are demonstrated in a nano-cone BE structure, as compared with those in a flat BE one. We have verified by systematic device simulations that the main cause of reduction in the performance parameters is the high electric field being more effectively concentrated at the tip of the cone-shaped BE. The greatly improved nonlinearity of the nano-cone resistive memory cell will be beneficial in the ultra-high-density crossbar array.

Ultralow power switching in a silicon-rich SiNy/SiNx double-layer resistive memory device.

Here we demonstrate low-power resistive switching in a Ni/SiNy/SiNx/p++-Si device by proposing a double-layered structure (SiNy/SiNx), where the two SiN layers have different trap densities. The LRS was measured to be as low as 1 nA at a voltage of 1 V, because the SiNx layer maintains insulating properties for the LRS. The single-layered device suffers from uncontrollability of the conducting path, accompanied by the inherent randomness of switching parameters, weak immunity to breakdown during the reset process, and a high operating current. On the other hand, for a double-layered device, the effective conducting path in each layer, which can determine the operating current, can be well controlled by the ICC during the initial forming and set processes. A one-step forming and progressive reset process is observed for a low-power mode, which differs from the high-power switching mode that shows a two-step forming and reset process. Moreover, nonlinear behavior in the LRS, whose origin can be attributed to the P-F conduction and F-N tunneling driven by abundant traps in the silicon-rich SiNx layer, would be beneficial for next-generation nonvolatile memory applications by using a conventional passive SiNx layer as an active dielectric.

Configurable Synaptic and Stochastic Neuronal Functions in ZnTe-Based Memristor for an RBM Neural Network.

This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two-terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low-current level (µA) in the forming process, a stable memory-switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired-pulse facilitation/depression, potentiation/depression, spike-amplitude-dependent plasticity, and spike-number-dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high-current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free-drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model.

Definition of a Localized Conducting Path via Suppressed Charge Injection in Oxide Memristors for Stable Practical Hardware Neural Networks.

Oxide-based memristors have been demonstrated as suitable options for memory components in neuromorphic systems. In such devices, the resistive switching characteristics are caused by the formation of conductive filaments (CFs) comprising oxygen vacancies. Thus, the electrical performance is primarily governed by the CF structure. Despite various approaches for regulating the oxygen vacancy distributions in oxide memristors, controlling the CF structure without modifying the device configuration related to material compatibility is still a challenge. This study demonstrates an effective strategy for localizing CF distributions in memristors by suppressing charge injection during the formation of conducting paths. As the injected charge quantity is reduced in the electroforming process of the oxide memristor, the CF distributions become narrower, leading to more reproducible and stable resistive switching characteristics in the device. Based on these findings, a reliable hardware neural network comprising oxide memristors is constructed to recognize complex images. The developed memristor has been employed as a synaptic memory component in systems without degradation for a long time. This promising concept of oxide memristors acting as stable synaptic components holds great potential for developing practical neuromorphic systems and their expansion into artificial intelligent systems.

Reliable organic memristors for neuromorphic computing by predefining a localized ion-migration path in crosslinkable polymer.

In flexible neuromorphic systems for realizing artificial intelligence, organic memristors are essential building blocks as artificial synapses to perform information processing and memory. Despite much effort to implement artificial neural networks (ANNs) using organic memristors, the reliability of these devices is inherently hampered by global ion transportation and arbitrary growth of conductive filaments (CFs). As a result, the performance of ANNs is restricted. Herein, a novel concept for confining CF growth in organic memristors is demonstrated by exploiting the unique functionality of crosslinkable polymers. This can be achieved by predefining the localized ion-migration path (LIP) in crosslinkable polymers. In the proposed organic memristor, metal cations are locally transported along the LIP. Thus, CF growth is achieved only in a confined region. A flexible memristor with an LIP exhibits a vastly improved reliability and uniformity, and it is capable of operating with high mechanical and electrical endurance. Moreover, neuromorphic arrays based on the proposed memristor exhibit 96.3% learning accuracy, which is comparable to the ideal software baseline. The proposed concept of predefining the LIP in organic memristors is expected to provide novel platforms for the advance of flexible electronics and to realize a variety of practical neural networks for artificial intelligence applications.

Realization of Biomimetic Synaptic Functions in a One-Cell Organic Resistive Switching Device Using the Diffusive Parameter of Conductive Filaments.

Toward the successful development of artificial intelligence, artificial synapses based on resistive switching devices are essential ingredients to perform information processing in spiking neural networks. In neural processes, synaptic plasticity related to the history of neuron activity plays a critical role during learning. In resistive switching devices, it is barely possible to emulate both short-term plasticity and long-term plasticity due to the uncontrollable dynamics of the conductive filaments (CFs). Despite extensive effort to realize synaptic plasticity in such devices, it is still challenging to achieve reliable synaptic functions due to the overgrowth of CFs in a random fashion. Herein, we propose an organic resistive switching device with bio-realistic synaptic functions by adjusting the CF diffusive parameter. In the proposed device, complete synaptic plasticity provides the history-dependent change in the conductance. Moreover, the homeostatic feedback, which resembles the biological process, regulates CF growth in our device, which enhances the reliability of synaptic plasticity. This novel concept for realizing synaptic functions in organic resistive switching devices may provide a physical platform to advance the fundamental understanding of learning and memory mechanisms and develop a variety of neural circuits and neuromorphic systems that can be linked to artificial intelligence and next-generation computing paradigm.

Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate.

Brain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate the coexistence of filamentary (abrupt) and interface (homogeneous) switching of Ni/SiN/BN/n++-Si devices. A better gradual conductance modulation is achieved for interface-type switching as compared with filamentary switching for an artificial synaptic device using appropriate voltage pulse stimulations. The improved classification accuracy for the interface switching (85.6%) is confirmed and compared to the accuracy of the filamentary switching mode (75.1%) by a three-layer neural network (784 × 128 × 10). Furthermore, the spike-timing-dependent plasticity characteristics of the synaptic device are also demonstrated. The results indicate the possibility of achieving an artificial synapse with a bilayer SiN/BN structure.

Synaptic Characteristics of Amorphous Boron Nitride-Based Memristors on a Highly Doped Silicon Substrate for Neuromorphic Engineering.

In this study, the resistive switching and synaptic properties of a complementary metal-oxide semiconductor-compatible Ti/a-BN/Si device are investigated for neuromorphic systems. A gradual change in resistance is observed in a positive SET operation in which Ti diffusion is involved in the conducting path. This operation is extremely suitable for synaptic devices in hardware-based neuromorphic systems. The isosurface charge density plots and experimental results confirm that boron vacancies can help generate a conducting path, whereas the conducting path generated by a Ti cation from interdiffusion forms is limited. A negative SET operation causes a considerable decrease in the formation energy of only boron vacancies, thereby increasing the conductivity in the low-resistance state, which may be related to RESET failure and poor endurance. The pulse transient characteristics, potentiation and depression characteristics, and good retention property of eight multilevel cells also indicate that the positive SET operation is more suitable for a synaptic device owing to the gradual modulation of conductance. Moreover, pattern recognition accuracy is examined by considering the conductance values of the measured data in the Ti/a-BN/Si device as the synaptic part of a neural network. The linear and symmetric synaptic weight update in a positive SET operation with an incremental voltage pulse scheme ensures higher pattern recognition accuracy.

Dual Functions of V/SiOx/AlOy/p++Si Device as Selector and Memory.

This letter presents dual functions including selector and memory switching in a V/SiOx/AlOy/p++Si resistive memory device by simply controlling compliance current limit (CCL). Unidirectional threshold switching is observed after a positive forming with low CCL of 1 µA. The shifts to the V-electrode side of the oxygen form the VOx layer, where the threshold switching can be explained by the metal-insulation-transition phenomenon. For higher CCL (30 µA) applied to the device, a bipolar memory switching is obtained, which is attributed to formation and rupture of the conducting filament in SiOy layer. 1.5-nm-thick AlOy layer with high thermal conductivity plays an important role in lowering the off-current for memory and threshold switching. Through the temperature dependence, high-energy barrier (0.463 eV) in the LRS is confirmed, which can cause nonlinearity in a low-resistance state. The smaller the CCL, the higher the nonlinearity, which provides a larger array size in the cross-point array. The coexistence of memory and threshold switching in accordance with the CCL provides the flexibility to control the device for its intended use.

Neuronal dynamics in HfOx/AlOy-based homeothermic synaptic memristors with low-power and homogeneous resistive switching.

We studied the pseudo-homeothermic synaptic behaviors by integrating complimentary metal-oxide-semiconductor-compatible materials (hafnium oxide, aluminum oxide, and silicon substrate). A wide range of temperatures, from 25 °C up to 145 °C, in neuronal dynamics was achieved owing to the homeothermic properties and the possibility of spike-induced synaptic behaviors was demonstrated, both presenting critical milestones for the use of emerging memristor-type neuromorphic computing systems in the near future. Biological synaptic behaviors, such as long-term potentiation, long-term depression, and spike-timing-dependent plasticity, are developed systematically, and comprehensive neural network analysis is used for temperature changes and to conform spike-induced neuronal dynamics, providing a new research regime of neurocomputing for potentially harsh environments to overcome the self-heating issue in neuromorphic chips.

Self-Compliant Bipolar Resistive Switching in SiN-Based Resistive Switching Memory.

Here, we present evidence of self-compliant and self-rectifying bipolar resistive switching behavior in Ni/SiNx/n⁺ Si and Ni/SiNx/n++ Si resistive-switching random access memory devices. The Ni/SiNx/n++ Si device's Si bottom electrode had a higher dopant concentration (As ion > 1019 cm-3) than the Ni/SiNx/n⁺ Si device; both unipolar and bipolar resistive switching behaviors were observed for the higher dopant concentration device owing to a large current overshoot. Conversely, for the device with the lower dopant concentration (As ion < 1018 cm-3), self-rectification and self-compliance were achieved owing to the series resistance of the Si bottom electrode.

Analog Synaptic Behavior of a Silicon Nitride Memristor.

In this paper, we present a synapse function using analog resistive-switching behaviors in a SiNx-based memristor with a complementary metal-oxide-semiconductor compatibility and expandability to three-dimensional crossbar array architecture. A progressive conductance change is attainable as a result of the gradual growth and dissolution of the conducting path, and the series resistance of the AlOy layer in the Ni/SiNx/AlOy/TiN memristor device enhances analog switching performance by reducing current overshoot. A continuous and smooth gradual reset switching transition can be observed with a compliance current limit (>100 µA), and is highly suitable for demonstrating synaptic characteristics. Long-term potentiation and long-term depression are obtained by means of identical pulse responses. Moreover, symmetric and linear synaptic behaviors are significantly improved by optimizing pulse response conditions, which is verified by a neural network simulation. Finally, we display the spike-timing-dependent plasticity with the multipulse scheme. This work provides a possible way to mimic biological synapse function for energy-efficient neuromorphic systems by using a conventional passive SiNx layer as an active dielectric.