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The intrinsic variability of memristor switching behavior can be used as a natural source of randomness, this variability is valuable for safe applications in hardware, such as the true random number generator (TRNG). However, the speed of TRNG is still be further improved. Here, we propose a reliable Ag/SiNx/n-Si volatile memristor, which exhibits a typical threshold switching device with stable repeat ability and fast switching speed. This volatile-memristor-based TRNG is combined with nonlinear feedback shift register (NFSR) to form a new type of high-speed dual output TRNG. Interestingly, the bit generation rate reaches a high speed of 112 kb/s. In addition, this new TRNG passed all 15 National Institute of Standards and Technology (NIST) randomness tests without post-processing steps, proving its performance as a hardware security application. This work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security. [ABSTRACT FROM AUTHOR]

Neuromorphic computing aims to achieve artificial intelligence by mimicking the mechanisms of biological neurons and synapses that make up the human brain. However, the possibility of using one reconfigurable memristor as both artificial neuron and synapse still requires intensive research in detail. In this work, Ag/SrTiO3(STO)/Pt memristor with low operating voltage is manufactured and reconfigurable as both neuron and synapse for neuromorphic computing chip. By modulating the compliance current, two types of resistance switching, volatile and nonvolatile, can be obtained in amorphous STO thin film. This is attributed to the manipulation of the Ag conductive filament. Furthermore, through regulating electrical pulses and designing bionic circuits, the neuronal functions of leaky integrate and fire, as well as synaptic biomimicry with spike-timing-dependent plasticity and paired-pulse facilitation neural regulation, are successfully realized. This study shows that the reconfigurable devices based on STO thin film are promising for the application of neuromorphic computing systems. [ABSTRACT FROM AUTHOR]

Biologically inspired neuromorphic sensory memory systems based on memristor have received a lot of attention in the booming artificial intelligence industry due to significant potential to effectively process multi‐sensory signals from complex external environments. However, many memristors have significant switching parameters disperse, which is a great challenge for using memristors in bionic neuromorphic sensory memory systems. Herein, a stable ferroelectric memristor based on the Pd/BaTiO3:Eu2O3/La0.67Sr0.33MnO3 grown on Silicon structure with SrTiO3 as buffer layer is presented. The device possesses low coercive field voltage (−1.3–2.1 V) and robust endurance characteristic (~1010 cycles) through optimizing the growth temperature. More importantly, an ultra‐stable artificial multimodal sensory memory system with visual and tactile functions was reported for the first time by combining a pressure sensor, a photosensitive sensor, and a robotic arm. Utilizing the above system, the sensitivity value of the system is expressed by the conductance of the memristor to realize the gradual change of external stimulus, and multi signals inputs at the same time to this system have faithfully achieved sensory adaptation to multimodal sensors. This work paves the way for future development of memristor‐based perception systems in efficient multisensory neural robots. [ABSTRACT FROM AUTHOR]

As a nanoscale semiconductor memory device, a ferroelectric memristor has promising prospects to break through the von Neumann framework in terms of artificial synaptic function, information processing, and integration. This study presents the fabrication of Li0.09Bi0.91FeO3 as the functional layer for a memristor device based on the Si substrate, enabling the integration of silicon complementary metal oxide semiconductor technology. In addition, it exhibits bipolar resistance switching characteristics in a direct current mode and can rapidly achieve stable conductance tunability at higher frequencies through the applied pulse for biosynapse simulation. More importantly, multiple devices are connected into electrical circuits to realize storage functions with information processing and programmable characteristics. This work paves the way for near-future applications of ferroelectric memristors in information processing. [ABSTRACT FROM AUTHOR]

Memristors with threshold switching behavior are increasingly used in the study of neuromorphic computing, which are frequently used to simulate synaptic functions due to their high integration and simple structure. However, building a neuron circuit to simulate the characteristics of biological neurons is still a challenge. In this work, we demonstrate a leaky integrate-and-fire model of neurons, which is presented by a memristor-CMOS hybrid circuit based on a threshold device of a TiN/HfO2/InGaZnO4/Si structure. Moreover, we achieve multiple neural functions based on the neuron model, including leaky integration, threshold-driven fire, and strength-modulated spike frequency characteristics. This work shows that HfO2-based threshold devices can realize the basic functions of spiking neurons and have great potential in artificial neural networks. [ABSTRACT FROM AUTHOR]