Neuromorphic computing is a promising alternative to conventional computing systems as it could enable parallel computation and adaptive learning process. However, the development of energy efficient neuromorphic hardware systems has been hindered by the limited performance of analog synaptic devices. Here, we demonstrate the analog conductance modulation behavior in the ferroelectric thin-film transistors (FeTFT) that have the nanoscale ferroelectric material and oxide semiconductors. Accurate control of polarization changes in the nanoscale ferroelectric layer induces conductance modulation to demonstrate linear potentiation and depression characteristics of FeTFTs. Our devices show potentiation and depression properties, including high linearity, multiple states, and small cycle-to-cycle/device-to-device variations. In simulations with measured properties, a neuromorphic system with FeTFT achieves 91.1% recognition accuracy of handwritten digits. This work may provide a way to realize the neuromorphic hardware systems that use FeTFTs as the synaptic devices.

Ferroelectric Analog Synaptic Transistors

PURPOSE: A resistive random access memory device is provided to prevent sneak path problem by using a thin film structure which directly connects a bi-directional switching layer on a resistance change layer. CONSTITUTION: A first electrode(10) is formed on a thin film layer(20). The thin film layer includes a resistance change layer and a switching layer which are connected with each other. The resistance change layer includes materials with bipolar resistance switching characteristics. The switching layer includes materials with bi-directional switching characteristics. The thin film layer is formed on a second electrode(30).

Resistance random access memory

Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non–von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications. Progress toward the integration of memristive devices in commercial solid-state electronic circuits and other potential applications will depend on performance and reliability challenges that still need to be addressed, as described here. Description Putting memristors to work Memristors, which are resistors that change conductivity and act as memories, are not only being used in commercial computing but have several application areas in computing and communications. Lanza et al. review how devices such as phase-change memories, resistive random-access memories, and magnetoresistive random-access memories are being integrated into silicon electronics. Memristors also are finding use in artificial intelligence when integrated in three-dimensional crossbar arrays for low-power, non–von Neuman architectures. Other applications include random-number generation for data encryption and radiofrequency switches for mobile communications. —PDS A review explains how resistors with memory functions are being integrated into electronics and new computer architectures. BACKGROUND Memristive devices exhibit an electrical resistance that can be adjusted to two or more nonvolatile levels by applying electrical stresses. The core of the most advanced memristive devices is a metal/insulator/metal nanocell made of phase-change, metal-oxide, magnetic, or ferroelectric materials, which is often placed in series with other circuit elements (resistor, selector, transistor) to enhance their performance in array configurations (i.e., avoid damage during state transition, minimize intercell disturbance). The memristive effect was discovered in 1969 and the first commercial product appeared in 2006, consisting of a 4-megabit nonvolatile memory based on magnetic materials. In the past few years, the switching endurance, data retention time, energy consumption, switching time, integration density, and price of memristive nonvolatile memories has been remarkably improved (depending on the materials used, values up to ~1015 cycles, >10 years, ~0.1 pJ, ~10 ns, 256 gigabits per die, and ≤$0.30 per gigabit have been achieved). ADVANCES As of 2021, memristive memories are being used as standalone memory and are also embedded in application-specific integrated circuits for the Internet of Things (smart watches and glasses, medical equipment, computers), and their market value exceeds $621 million. Recent studies have shown that memristive devices may also be exploited for advanced computation, data security, and mobile communication. Advanced computation refers to the hardware implementation of artificial neural networks by exploiting memristive attributes such as progressive conductance increase and decrease, vector matrix multiplication (in crossbar arrays), and spike timing–dependent plasticity; state-of-the-art developments have achieved >10 trillion operations per second per watt. Data encryption can be realized by exploiting the stochasticity inherent in the memristive effect, which manifests as random fluctuations (within a given range) of the switching voltages/times and state currents. For example, true random number generator and physical unclonable functions produce random codes when exposing a population of memristive devices to an electrical stress at 50% of switching probability (it is impossible to predict which devices will switch because that depends on their atomic structure). Mobile communication can also benefit from memristive devices because they could be employed as 5G and terahertz switches with low energy consumption owing to the nonvolatile nature of the resistive states; the current commercial technology is based on silicon transistors, but they are volatile and consume data both during switching and when idle. State-of-the-art developments have achieved cutoff frequencies of >100 THz with excellent insertion loss and isolation. OUTLOOK Consolidating memristive memories in the market and creating new commercial memristive technologies requires further enhancement of their performance, integration density, and cost, which may be achieved via materials and structure engineering. Market forecasts expect the memristive memories market to grow up to ~$5.6 billion by 2026, which will represent ~2% of the nearly $280 billion memory market. Phase-change and metal-oxide memristive memories should improve switching endurance and reduce energy consumption and variability, and the magnetic ones should offer improved integration density. Ferroelectric memristive memories still suffer low switching endurance, which is hindering commercialization. The figures of merit of memristive devices for advanced computation highly depend on the application, but maximizing endurance, retention, and conductance range while minimizing temporal conductance fluctuations are general goals. Memristive devices for data encryption and mobile communication require higher switching endurance, and two-dimensional materials prototypes are being investigated. Part of Science’s coverage of the 75th anniversary of the discovery of the transistor Fundamental memristive effects and their applications. Memristive devices, in which electrical resistance can be adjusted to two or more nonvolatile levels, can be fabricated using different materials (top row). This allows adjusting their performance to fulfill the requirements of different technologies. Memristive memories are a reality, and important progress is being achieved in advanced computation, security systems, and mobile communication (bottom row).

Memristive technologies for data storage, computation, encryption, and radio-frequency communication

Exploring Ferroelectric Switching in α-In2Se3 for Neuromorphic Computing

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices.

Next-generation non-volatile memories with ultrafast speed, low power consumption, and high density are highly desired in the era of big data. Here, we report a high performance memristor based on a Ag/BaTiO3/Nb:SrTiO3 ferroelectric tunnel junction (FTJ) with the fastest operation speed (600 ps) and the highest number of states (32 states or 5 bits) per cell among the reported FTJs. The sub-nanosecond resistive switching maintains up to 358 K, and the write current density is as low as 4 × 103 A cm−2. The functionality of spike-timing-dependent plasticity served as a solid synaptic device is also obtained with ultrafast operation. Furthermore, it is demonstrated that a Nb:SrTiO3 electrode with a higher carrier concentration and a metal electrode with lower work function tend to improve the operation speed. These results may throw light on the way for overcoming the storage performance gap between different levels of the memory hierarchy and developing ultrafast neuromorphic computing systems. Memristor devices based on ferroelectric tunnel junctions are promising, but suffer from quite slow switching times. Here, the authors report on ultrafast switching times at and above room temperature of 600ps in Ag/BaTiO3/Nb:SrTiO3 based ferroelectric tunnel junctions.

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Sub-nanosecond memristor based on ferroelectric tunnel junction.

The rapid development of neuro-inspired computing demands synaptic devices with ultrafast speed, low power consumption, and multiple non-volatile states, among other features. Here, a high-performance synaptic device is designed and established based on a Ag/PbZr0.52Ti0.48O3 (PZT, (111)-oriented)/Nb:SrTiO3 ferroelectric tunnel junction (FTJ). The advantages of (111)-oriented PZT (~1.2 nm) include its multiple ferroelectric switching dynamics, ultrafine ferroelectric domains, and small coercive voltage. The FTJ shows high-precision (256 states, 8 bits), reproducible (cycle-to-cycle variation, ~2.06%), linear (nonlinearity <1) and symmetric weight updates, with a good endurance of >109 cycles and an ultralow write energy consumption. In particular, manipulations among 150 states are realized under subnanosecond (~630 ps) pulse voltages ≤5 V, and the fastest resistance switching at 300 ps for the FTJs is achieved by voltages <13 V. Based on the experimental performance, the convolutional neural network simulation achieves a high online learning accuracy of ~94.7% for recognizing fashion product images, close to the calculated result of ~95.6% by floating-point-based convolutional neural network software. Interestingly, the FTJ-based neural network is very robust to input image noise, showing potential for practical applications. This work represents an important improvement in FTJs towards building neuro-inspired computing systems. 

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High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing

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Flexible ferroelectric devices have been a hot-spot topic because of their potential wearable applications as nonvolatile memories and sensors. Here, high-quality (111)-oriented BiFeO3 ferroelectri...

Xi Jin

Papers

Sub-nanosecond memristor based on ferroelectric tunnel junction.

High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing

High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing

BiFeO3-Based Flexible Ferroelectric Memristors for Neuromorphic Pattern Recognition

Efficient Parallel Multi-Bit Logic-in-Memory Based on a Ultrafast Ferroelectric Tunnel Junction Memristor