Improvement of Conductance Modulation Linearity in a Cu2+-Doped KNbO3 Memristor through the Increase of the Number of Oxygen Vacancies

Hardware implementation of memristor-based artificial neural networks

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Role of oxygen vacancies in ferroelectric or resistive switching hafnium oxide

HfO2 shows promise for emerging ferroelectric and resistive switching (RS) memory devices owing to its excellent electrical properties and compatibility with complementary metal oxide semiconductor technology based on mature fabrication processes such as atomic layer deposition. Oxygen vacancy (Vo), which is the most frequently observed intrinsic defect in HfO2-based films, determines the physical/electrical properties and device performance. Vo influences the polymorphism and the resulting ferroelectric properties of HfO2. Moreover, the switching speed and endurance of ferroelectric memories are strongly correlated to the Vo concentration and redistribution. They also strongly influence the device-to-device and cycle-to-cycle variability of integrated circuits based on ferroelectric memories. The concentration, migration, and agglomeration of Vo form the main mechanism behind the RS behavior observed in HfO2, suggesting that the device performance and reliability in terms of the operating voltage, switching speed, on/off ratio, analog conductance modulation, endurance, and retention are sensitive to Vo. Therefore, the mechanism of Vo formation and its effects on the chemical, physical, and electrical properties in ferroelectric and RS HfO2 should be understood. This study comprehensively reviews the literature on Vo in HfO2 from the formation and influencing mechanism to material properties and device performance. This review contributes to the synergetic advances of current knowledge and technology in emerging HfO2-based semiconductor devices.

Realization of Artificial Nerve Synapses Based on Biological Threshold Resistive Random Access Memory

A one-selector one resistor (1S1R) array formed of a selector and resistive random access memory (RRAM) is an important way to achieve high-density storage and neuromorphic computing. However, the low durability and poor consistency of the selector limit its practical application. The fabrication of a selector based on egg albumen (EA) is reported in this paper. The device exhibits excellent bidirectional threshold switching characteristics, including a low leakage current (10-7  A), a high ON/OFF current ratio (106 ), and good endurance (>700 days). It is used as a selector to form a 1S1R unit in combination with an EA-based RRAM to effectively solve the leakage current in a crossbar array. A feasible solution is provided for the realization of a protein-based 1S1R array to achieve high-density storage. The 1S1R unit shows characteristics similar to those of synapses in the human brain under impulse excitation and has great potential in simulating the human brain for neuromorphic calculations.).

Effect of Post-Annealing on Barrier Modulations in Pd/IGZO/SiO2/p+-Si Memristors

In this article, we study the post-annealing effect on the synaptic characteristics in Pd/IGZO/SiO₂/p⁺-Si memristor devices. The O-H bond in IGZO films affects the switching characteristics that can be controlled by the annealing process. We propose a switching model based on using a native oxide as the Schottky barrier. The barrier height is extracted by the conduction mechanism of thermionic emission in samples with different annealing temperatures. Additionally, the change in conductance is explained by an energy band diagram including trap models. The activation energy is obtained by the depression curve of the samples with different annealing temperatures to better understand the switching mechanism. Moreover, our results reveal that the annealing temperature and retention can affect the linearity of potentiation and depression. Finally, we investigate the effect of the annealing temperature on the recognition rate of MNIST in the proposed neural network.

Ion-Movement-Based Synaptic Device for Brain-Inspired Computing

As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices.

Impact of Electrolyte Incorporation in Anodized Niobium on Its Resistive Switching

The aim of this study was to develop memristors based on Nb₂O₅ grown by a simple and inexpensive electrochemical anodization process. It was confirmed that the electrolyte selection plays a crucial role in resistive switching due to electrolyte species incorporation in oxide, thus influencing the formation of conductive filaments. Anodic memristors grown in phosphate buffer showed improved electrical characteristics, while those formed in citrated buffer exhibited excellent memory capabilities. The chemical composition of oxides was successfully determined using HAXPES, while their phase composition and crystal structure with conductive filaments was assessed by TEM at the nanoscale. Overall, understanding the switching mechanism leads towards a wide range of possible applications for Nb memristors either as selector devices or nonvolatile memories.

Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr0.7Ca0.3MnO3 Memristor

An amorphous Pr_0.7Ca_0.3MnO₃ (PCMO) film was grown on a TiN/SiO₂/Si (TiN–Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.

Oxygen vacancy engineering of TaOx-based resistive memories by Zr doping for improved variability and synaptic behavior

Resistive switching (RS) devices are promising forms of non-volatile memory. However, one of the biggest challenges for RS memory applications is the device-to-device (D2D) variability, which is related to the intrinsic stochastic formation and configuration of oxygen vacancy (V_O) conductive filaments (CFs). In order to reduce the D2D variability, control over the formation and configuration of oxygen vacancies is paramount. In this study, we report on the Zr doping of TaO_x-based RS devices prepared by pulsed-laser deposition as an efficient means of reducing the V_Oformation energy and increasing the confinement of CFs, thus reducing D2D variability. Our findings were supported by XPS, spectroscopic ellipsometry and electronic transport analysis. Zr-doped films showed increased V_Oconcentration and more localized V_Os, due to the interaction with Zr. DC and pulse mode electrical characterization showed that the D2D variability was decreased by a factor of seven, the resistance window was doubled, and a more gradual and monotonic long-term potentiation/depression in pulse switching was achieved in forming-free Zr:TaO_xdevices, thus displaying promising performance for artificial synapse applications.