Abstract
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.
Highlights
The rapid development of neuro-inspired computing demands synaptic devices with ultrafast speed, low power consumption, and multiple non-volatile states, among other features
The basic tasks of recognizing handwritten digits in the Modified National Institute of Standards and Technology (MNIST) database and more complicated fashion product images in the Fashion-MNIST (F-MNIST) database have been widely used to test the capability of neural network computing systems[17,18]
Pt/BaTiO3 (001)/Nb:SrTiO3 (NSTO)based ferroelectric tunnel junction (FTJ) with 200 states in a conductance dynamic range of 10× have been reported with an operation speed of 50 ns and an endurance of >1.1 × 104 cycles[24]
Summary
The rapid development of neuro-inspired computing demands synaptic devices with ultrafast speed, low power consumption, and multiple non-volatile states, among other features. 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). It is commonly believed that to realize an efficient neuro-inspired computing system, it is necessary to develop high-performance synaptic devices that are based on memristors and capable of emulating the weight updates of biological synapses[4,5]. As a recently developed memristor strategy, the ferroelectric tunnel junction (FTJ) is in principle a promising candidate for building high-performance artificial synapses. This is because the FTJ stores data non-volatilely and intrinsically as ferroelectric polarization states in its ultrathin ferroelectric barrier. Based on the principle of the FTJ, multiple conductance states are related to multiple ferroelectric domains
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