Biodegradable and flexible i-carrageenan based RRAM with ultralow power consumption

Transient memories,which can physically disappear without leaving traceable remains over a period of normal operation,are attracting increasing attention for potential applications in the fields of data security and green electronics.Resistive random access memory(RRAM)is a promising candidate for next-generation memory.In this context,biocompatible l-carrageenan(l-car),extracted from natural seaweed,is introduced for the fabrication of RRAM devices(Ag/l-car/Pt).Taking advantage of the complexation processes between the functional groups(C–O–C,C–O–H,et al.)and Ag metal ions,a lower migration barrier of Ag ions and a high-speed switching(22.2 ns for SET operation/26 ns for RESET operation)were achieved,resulting in an ultralow power consumption of 56 fJ.And the prepared Ag/l-car/Pt RRAM devices also revealed the capacities of multilevel storage and flexibility.In addition,thanks to the hydrophilic groups of l-car molecule,the RRAM devices can be rapidly dissolved in deionized(DI)water within 13 minutes,showing excellent transient characteristics.This work demonstrates that l-car based RRAM devices have great potential for applications in secure storage applications,flexible electronics and transient electronics.

Neutron irradiation-induced effects on the reliability performance of electrochemical metallization memory devices

In this work,electrochemical metallization memory(ECM)devices with an Ag/AgInSbTe(AIST)/amorphous carbon(a-C)/Pt structure were irradiated with 14 MeV neutrons.The switching reliability performance before and after neutron irradiation was compared and analyzed in detail.The results show that the irradiated memory cells functioned properly,and the initial resistance,the resistance at the low-resistance state(LRS),the RESET voltage and the data retention performance showed little degradation even when the total neutron fluence was as high as 2.5×1011 n/cm2.Other switching characteristics such as the forming voltage,the resistance at the high-resistance state(HRS),and the SET voltage were also studied,all of which merely showed a slight parameter drift.Irradiation-induced Ag ions doping of the a-C layer is proposed to explain the damaging effects of neutron irradiation.The excellent hard characteristics of these Ag/AIST/a-C/Pt-based ECM devices suggest potential beneficial applications in the aerospace and nuclear industries.

Voltage-dependent plasticity and image Boolean operations realized in a WOx-based memristive synapse

The development of electronic devices that possess the functionality of biological synapses is a crucial step towards neuromorphic computing.In this work,we present a WOx-based memristive device that can emulate voltage-dependent synaptic plasticity.By adjusting the amplitude of the applied voltage,we were able to reproduce short-term plasticity(STP)and the transition from STP to long-term potentiation.The stimulation with high intensity induced long-term enhancement of conductance without any decay process,thus representing a permanent memory behavior.Moreover,the image Boolean operations(including intersection,subtraction,and union)were also demonstrated in the memristive synapse array based on the above voltage-dependent plasticity.The experimental achievements of this study provide a new insight into the successful mimicry of essential characteristics of synaptic behaviors.

Flexible and degradable resistive switching memory fabricated with sodium alginate

Transient electronics has attracted interest as an emerging technology to solve electronic-waste problem,due to its physically vanishing ability in solution.Here in this work,we demonstrate a flexible and degradable transient resistive switching(RS) memory device with simple structure of Cu/sodium alginate(SA)/ITO.The device presents excellent RS characteristics as well as high flexibility,including low operating voltage(<1.5 V) and multilevel RS behavior.No performance degradation occurs after bending the device 50 times.Moreover,our device can be absolutely dissolved in deionized water.The proposed SA-based transient memory device has great potential for the development of green and security memory devices.

Emerging multimodal memristors for biorealistic neuromorphic applications

The integration of sensory information from different modalities,such as touch and vision,is essential for organisms to perform behavioral functions such as decision-making,learning,and memory.Artificial implementation of human multi-sensory perception using electronic supports is of great significance for achieving efficient human–machine interaction.Thanks to their structural and functional similarity with biological synapses,memristors are emerging as promising nanodevices for developing artificial neuromorphic perception.Memristive devices can sense multidimensional signals including light,pressure,and sound.Their in-sensor computing architecture represents an ideal platform for efficient multimodal perception.We review recent progress in multimodal memristive technology and its application to neuromorphic perception of complex stimuli carrying visual,olfactory,auditory,and tactile information.At the device level,the operation model and undergoing mechanism have also been introduced.Finally,we discuss the challenges and prospects associated with this rapidly progressing field of research.

Advances in memristor based artificial neuron fabrication-materials,models,and applications

Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.

Memristors with organic-inorganic halide perovskites

Organic-inorganic halide perovskites(OHPs)have been intensively studied for application in solar cells with high conversion efficiency exceeding 22%.The unique electrical and optical properties of OHPs have led to their use in optoelectronic device applications beyond photovoltaics,such as light-emitting diodes,photodetectors,transistors.New information storage technologies and computing architectures are being researched extensively with the aim of addressing the growing challenge of approaching end of Moore's law and von Neumann bottleneck.As the fourth basic circuit element,memristor is a leading candidate with powerful capabilities in information storage and neuromorphic computing applications.Recently,OHPs have received growing attention as promising materials for memristors.In particular,their mixed ionic-electronic conduction ability paired with light sensitivity provide OHPs with the opportunity to display novel functions such as optical-erase memory,optogenetics-inspired synaptic functions,and lightaccelerated learning capability.This review covers recent advances in OHP-based memristors development including memristive mechanism and analytical models,universal memristive characteristics for memory and neuromorphic computing applications,and novel multi-functionalization.Challenges and future prospects of OHP-based memristors are also discussed.