Synaptic Plasticity Selectively Activated by Polarization-Dependent Energy-Efficient Ion Migration in an Ultrathin Ferroelectric Tunnel Junction

Physics, Structures, and Applications of Fluorite-Structured Ferroelectric Tunnel Junctions

The interest in ferroelectric tunnel junctions (FTJ) has been revitalized by the discovery of ferroelectricity in fluorite-structured oxides such as HfO2 and ZrO2 . In terms of thickness scaling, CMOS compatibility, and 3D integration, these fluorite-structured FTJs provide a number of benefits over conventional perovskite-based FTJs. Here, recent developments involving all FTJ devices with fluorite structures are examined. The transport mechanism of fluorite-structured FTJs is explored and contrasted with perovskite-based FTJs and other 2-terminal resistive switching devices starting with the operation principle and essential parameters of the tunneling electroresistance effect. The applications of FTJs, such as neuromorphic devices, logic-in-memory, and physically unclonable function, are then discussed, along with several structural approaches to fluorite-structure FTJs. Finally, the materials and device integration difficulties related to fluorite-structure FTJ devices are reviewed. The purpose of this review is to outline the theories, physics, fabrication processes, applications, and current difficulties associated with fluorite-structure FTJs while also describing potential future possibilities for optimization.

Giant tunnelling electroresistance in atomic-scale ferroelectric tunnel junctions

Ferroelectric tunnel junctions are promising towards high-reliability and low-power non-volatile memories and computing devices. Yet it is challenging to maintain a high tunnelling electroresistance when the ferroelectric layer is thinned down towards atomic scale because of the ferroelectric structural instability and large depolarization field. Here we report ferroelectric tunnel junctions based on samarium-substituted layered bismuth oxide, which can maintain tunnelling electroresistance of 7 × 105 with the samarium-substituted bismuth oxide film down to one nanometer, three orders of magnitude higher than previous reports with such thickness, owing to efficient barrier modulation by the large ferroelectric polarization. These ferroelectric tunnel junctions demonstrate up to 32 resistance states without any write-verify technique, high endurance (over 5 × 109), high linearity of conductance modulation, and long retention time (10 years). Furthermore, tunnelling electroresistance over 109 is achieved in ferroelectric tunnel junctions with 4.6-nanometer samarium-substituted bismuth oxide layer, which is higher than commercial flash memories. The results show high potential towards multi-level and reliable non-volatile memories.

Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing

Ferroelectric tunnel junctions (FTJs) have been regarded as one of the most promising candidates for next-generation devices for data storage and neuromorphic computing owing to their advantages such as fast operation speed, low energy consumption, convenient 3D stack ability, etc. Here, dramatically different from the conventional engineering approaches, we have developed a tunnel barrier decoration strategy to improve the ON/OFF ratio, where the ultrathin SrTiO3 (STO) dielectric layers are periodically mounted onto the BaTiO3 (BTO) ferroelectric tunnel layer using the high-throughput technique. The inserted STO enhances the local tetragonality of the BTO, resulting in a strengthened ferroelectricity in the tunnel layer, which greatly improves the OFF state and reduces the ON state. Combined with the optimized oxygen migration, which can further manipulate the tunneling barrier, a record-high ON/OFF ratio of ∼108 has been achieved. Furthermore, utilizing these FTJ-based artificial synapses, an artificial neural network has been simulated via back-propagation algorithms, and a classification accuracy as high as 92% has been achieved. This study screens out the prominent FTJ by the high-throughput technique, advancing the tunnel layer decoration at the atomic level in the FTJ design and offering a fundamental understanding of the multimechanisms in the tunnel barrier.

Short-term synaptic plasticity in emerging devices for neuromorphic computing

Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.

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.

A Review on Solution-Processed Organic Phototransistors and Their Recent Developments

Today, more disciplines are intercepting each other, giving rise to “cross-disciplinary” research. Technological advancements in material science and device structure and production have paved the way towards development of new classes of multi-purpose sensory devices. Organic phototransistors (OPTs) are photo-activated sensors based on organic field-effect transistors that convert incident light signals into electrical signals. The organic semiconductor (OSC) layer and three-electrode structure of an OPT offer great advantages for light detection compared to conventional photodetectors and photodiodes, due to their signal amplification and noise reduction characteristics. Solution processing of the active layer enables mass production of OPT devices at significantly reduced cost. The chemical structure of OSCs can be modified accordingly to fulfil detection at various wavelengths for different purposes. Organic phototransistors have attracted substantial interest in a variety of fields, namely biomedical, medical diagnostics, healthcare, energy, security, and environmental monitoring. Lightweight and mechanically flexible and wearable OPTs are suitable alternatives not only at clinical levels but also for point-of-care and home-assisted usage. In this review, we aim to explain different types, working mechanism and figures of merit of organic phototransistors and highlight the recent advances from the literature on development and implementation of OPTs for a broad range of research and real-life applications.

A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem

Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals' adaptability. However, enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment, ideally in a single device, is challenging. Here, we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative learning. This approach couples the photogating effect at the interface with ferroelectric polarization switching, enabling highly tunable optical modulation of charge carriers. Our device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to two. In particular, we obtain a large output difference (>103), which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless conduction. As proof-of-concept demonstrations, photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device, indicating compatibility with biological and digital data processing.

Wide range modulation of synaptic weight in thin-film transistors with hafnium oxide gate insulator and indium-zinc oxide channel layer for artificial synapse application

Wide range synaptic weight modulation with a tunable drain current was demonstrated in thin-film transistors (TFTs) with a hafnium oxide (HfO2-x) gate insulator and an indium-zinc oxide (IZO) channel layer for application to artificial synapses in neuromorphic systems. The drain current in these TFTs was reduced significantly by four orders of magnitude on application of a negative gate bias, then could be restored to its original value by applying a positive bias. The reduced drain current under negative biasing is interpreted as being caused by voltage-driven oxygen ion migration from the HfO2-x gate insulator to the IZO channel, which reduces the oxygen vacancy concentration in the IZO channel. In addition to emulating the analog-type potentiation and depression motions in artificial synapses, the tunable drain current presents paired-pulse facilitation and short-term and long-term plasticity behaviors. These wide-ranging and nonvolatile synaptic behaviors with tunable drain currents are indicative of the potential of the proposed TFTs for artificial synapse applications.

Giant Ferroelectric Resistance Switching Controlled by a Modulatory Terminal for Low-Power Neuromorphic In-Memory Computing

Ferroelectrics have been demonstrated as excellent building blocks for high-performance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in-memory computing. Here, it is reported that the emerging van der Waals ferroelectric α-In₂ Se₃ can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistance-switching ratio of heterosynaptic memristors above 10³ , which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α-In₂ Se₃ channel is responsible for the resistance switching at various paired terminals. The third terminal of α-In₂ Se₃ memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read-energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α-In₂ Se₃ neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy-efficient, brain-inspired computing systems and logic-in-memory computers.

Emerging Opportunities for 2D Semiconductor/Ferroelectric Transistor-Structure Devices

Semiconductor technology, which is rapidly evolving, is poised to enter a new era for which revolutionary innovations are needed to address fundamental limitations on material and working principle level. 2D semiconductors inherently holding novel properties at the atomic limit show great promise to tackle challenges imposed by traditional bulk semiconductor materials. Synergistic combination of 2D semiconductors with functional ferroelectrics further offers new working principles, and is expected to deliver massively enhanced device performance for existing complementary metal-oxide-semiconductor (CMOS) technologies and add unprecedented applications for next-generation electronics. Herein, recent demonstrations of novel device concepts based on 2D semiconductor/ferroelectric heterostructures are critically reviewed covering their working mechanisms, device construction, applications, and challenges. In particular, emerging opportunities of CMOS-process-compatible 2D semiconductor/ferroelectric transistor structure devices for the development of a rich variety of applications are discussed, including beyond-Boltzmann transistors, nonvolatile memories, neuromorphic devices, and reconfigurable nanodevices such as p-n homojunctions and self-powered photodetectors. It is concluded that 2D semiconductor/ferroelectric heterostructures, as an emergent heterogeneous platform, could drive many more exciting innovations for modern electronics, beyond the capability of ubiquitous silicon systems.

Controlled Sign Reversal of Electroresistance in Oxide Tunnel Junctions by Electrochemical-Ferroelectric Coupling

The persistence of ferroelectricity in ultrathin layers relies critically on screening or compensation of polarization charges which otherwise destabilize the ferroelectric state. At surfaces, charged defects play a crucial role in the screening mechanism triggering novel mixed electrochemical-ferroelectric states. At interfaces, however, the coupling between ferroelectric and electrochemical states has remained unexplored. Here, we make use of the dynamic formation of the oxygen vacancy profile in the nanometer-thick barrier of a ferroelectric tunnel junction to demonstrate the interplay between electrochemical and ferroelectric degrees of freedom at an oxide interface. We fabricate ferroelectric tunnel junctions with a La_{0.7}Sr_{0.3}MnO_{3} bottom electrode and BaTiO_{3} ferroelectric barrier. We use poling strategies to promote the generation and transport of oxygen vacancies at the metallic top electrode. Generated oxygen vacancies control the stability of the ferroelectric polarization and modify its coercive fields. The ferroelectric polarization, in turn, controls the ionization of oxygen vacancies well above the limits of thermodynamic equilibrium, triggering the build up of a Schottky barrier at the interface which can be turned on and off with ferroelectric switching. This interplay between electronic and electrochemical degrees of freedom yields very large values of the electroresistance (more than 10^{6}% at low temperatures) and enables a controlled switching between clockwise and counterclockwise switching modes in the same junction (and consequently, a change of the sign of the electroresistance). The strong coupling found between electrochemical and electronic degrees of freedom sheds light on the growing debate between resistive and ferroelectric switching in ferroelectric tunnel junctions, and moreover, can be the source of novel concepts in memory devices and neuromorphic computing.

Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.

Ferroelectric Tunnel Junctions: Modulations on the Potential Barrier

Recently, ferroelectric tunnel junctions (FTJs) have attracted considerable attention for potential applications in next-generation memories, owing to attractive advantages such as high-density of data storage, nondestructive readout, fast write/read access, and low energy consumption. Herein, recent progress regarding FTJ devices is reviewed with an emphasis on the modulation of the potential barrier. Electronic and ionic approaches that modulate the ferroelectric barriers themselves and/or induce extra barriers in electrodes or at ferroelectric/electrode interfaces are discussed with the enhancement of memory performance. Emerging physics, such as nanoscale ferroelectricity, resonant tunneling, and interfacial metallization, and the applications of FTJs in nonvolatile data storage, neuromorphic synapse emulation, and electromagnetic multistate memory are summarized. Finally, challenges and perspectives of FTJ devices are underlined.

Heterosynaptic Plasticity Emulated by Liquid Crystal-Carbon Nanotube Composites with Modulatory Interneurons

The aim of the neuromorphic computing is to emulate energy-efficient and smart data-processing ability of the biological brain, which is achieved by massively interconnected neurons and synapses. The strength of a connection between two neurons is modified by homosynaptic and heterosynaptic plasticity. As current research in the neuromorphic device is mainly focused on emulating homosynaptic plasticity, complex biological functions are not easy to mimic because they require both homosynaptic and heterosynaptic plasticity. We demonstrate the use of a liquid crystal-carbon nanotube (LC-CNT) composite as a resistive switching material that can emulate both the homosynaptic and heterosynaptic functions of biological neurons. The LC-CNT composite undergoes resistance change by CNT alignment and aggregated wire formation subjected to an applied electric field. A two-terminal device that exploits this mechanism achieves analog switching and homosynaptic potentiation. In a multiterminal device structure, the modulatory interneuron could tune the synaptic properties to perform heterosynaptic functions such as heterosynaptic potentiation, heterosynaptic facilitation, and synaptic weight normalization to emulate complex biological functions of a brain. Artificial synapses that exploit this multifunctionality of the LC-CNT composite have uses in next-generation neuromorphic devices.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Artificial Optoelectronic Synapses Based on Ferroelectric Field-Effect Enabled 2D Transition Metal Dichalcogenide Memristive Transistors

Neuromorphic visual sensory and memory systems, which can perceive, process, and memorize optical information, represent core technology for artificial intelligence and robotics with autonomous navigation. An optoelectronic synapse with an elegant integration of biometric optical sensing and synaptic learning functions can be a fundamental element for the hardware-implementation of such systems. Here, we report a class of ferroelectric field-effect memristive transistors made of a two-dimensional WS₂ semiconductor atop a ferroelectric PbZr_0.2Ti_0.8O₃ (PZT) thin film for optoelectronic synaptic devices. The WS₂ channel exhibits voltage- and light-controllable memristive switching, dependent on the optically and electrically tunable ferroelectric domain patterns in the underlying PZT layer. These devices consequently show the emulation of optically driven synaptic functionalities including both short- and long-term plasticity as well as the implementation of brainlike learning rules. Integration of these rich synaptic functionalities into one single artificial optoelectronic device could allow the development of future neuromorphic electronics capable of optical information sensing and learning.

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

The Pt/KNbO₃/TiN/Si (KN) memristor exhibits various biological synaptic properties. However, it also displays nonlinear conductance modulation with the application of identical pulses, indicating that it should be improved for neuromorphic applications. The abrupt change of the conductance originates from the inhomogeneous growth/dissolution of oxygen vacancy filaments in the KN film. The change of the filaments in a KN film is controlled by two mechanisms with different growth/dissolution rates: a redox process with a fast rate and an oxygen vacancy diffusion process with a slow rate. Therefore, the conductance modulation linearity can be improved if the growth/dissolution of the filaments is controlled by only one mechanism. When the number of oxygen vacancies in the KN film was increased through doping of Cu²⁺ ions, the growth/dissolution of the filaments in the Cu²⁺-doped KN (CKN) film was mainly influenced by the redox process of oxygen vacancies. Therefore, the CKN film exhibited improved conductance modulation linearity, confirming that the linearity of conductance modulation can be improved by increasing the number of oxygen vacancies in the memristor. This method can be applied to other memristors to improve the linearity of conductance modulation. The CKN memristor also provides excellent biological synaptic characteristics for neuromorphic computing systems.

Ferroelectric Tunneling Junctions Based on Aluminum Oxide/ Zirconium-Doped Hafnium Oxide for Neuromorphic Computing

Ferroelectric tunneling junctions (FTJs) with tunable tunneling electroresistance (TER) are promising for many emerging applications, including non-volatile memories and neurosynaptic computing. One of the key challenges in FTJs is the balance between the polarization value and the tunneling current. In order to achieve a sizable on-current, the thickness of the ferroelectric layer needs to be scaled down below 5 nm. However, the polarization in these ultra-thin ferroelectric layers is very small, which leads to a low tunneling electroresistance (TER) ratio. In this paper, we propose and demonstrate a new type of FTJ based on metal/Al₂O₃/Zr-doped HfO₂/Si structure. The interfacial Al₂O₃ layer and silicon substrate enable sizable TERs even when the thickness of Zr-doped HfO₂ (HZO) is above 10 nm. We found that F-N tunneling dominates at read voltages and that the polarization switching in HZO can alter the effective tunneling barrier height and tune the tunneling resistance. The FTJ synapses based on Al₂O₃/HZO stacks show symmetric potentiation/depression characteristics and widely tunable conductance. We also show that spike-timing-dependent plasticity (STDP) can be harnessed from HZO based FTJs. These novel FTJs will have high potential in non-volatile memories and neural network applications.

Ferroelectric Tunneling Junctions Based on Aluminum Oxide/ Zirconium-Doped Hafnium Oxide for Neuromorphic Computing

Ferroelectric tunneling junctions (FTJs) with tunable tunneling electroresistance (TER) are promising for many emerging applications, including non-volatile memories and neurosynaptic computing. One of the key challenges in FTJs is the balance between the polarization value and the tunneling current. In order to achieve a sizable on-current, the thickness of the ferroelectric layer needs to be scaled down below 5 nm. However, the polarization in these ultra-thin ferroelectric layers is very small, which leads to a low tunneling electroresistance (TER) ratio. In this paper, we propose and demonstrate a new type of FTJ based on metal/Al₂O₃/Zr-doped HfO₂/Si structure. The interfacial Al₂O₃ layer and silicon substrate enable sizable TERs even when the thickness of Zr-doped HfO₂ (HZO) is above 10 nm. We found that F-N tunneling dominates at read voltages and that the polarization switching in HZO can alter the effective tunneling barrier height and tune the tunneling resistance. The FTJ synapses based on Al₂O₃/HZO stacks show symmetric potentiation/depression characteristics and widely tunable conductance. We also show that spike-timing-dependent plasticity (STDP) can be harnessed from HZO based FTJs. These novel FTJs will have high potential in non-volatile memories and neural network applications.

Zileuton ameliorates depressive-like behaviors, hippocampal neuroinflammation, apoptosis and synapse dysfunction in mice exposed to chronic mild stress

Our previous study has found that zileuton, a selective 5-lipoxygenase (5LO) inhibitor, abrogated lipopolysaccharide-induced depressive-like behaviors and hippocampal neuroinflammation. Herein, we further extended our curiosity to investigate effects of zileuton on stress-induced depressive-like behaviors. Our data indicated that zileuton significantly ameliorated depressive-like behaviors in mice subjected to chronic mild stress (CMS), as shown in the tail suspension test, forced swimming test and novelty-suppressed feeding test. The further studies indicated that zileuton suppressed hippocampal neuroinflammation, evidenced by lower levels of TNF-α, IL-1β and nuclear NF-κB p65 as well as decreased number of Iba1-positive cells. It also significantly ameliorated hippocampal apoptosis, indicated by deceased number of TUNEL-positive cells, deceased ratio of cleaved caspase-3/procaspase-3 and increased ratio of Bcl-2/Bax. More importantly, zileuton increased the level of synaptic proteins PSD-95 and SYN and the number of NeuN⁺/BrdU⁺ cells in the hippocampus. Over all, zileuton alleviated CMS-induced depressive-like behaviors, neuroinflammatory and apoptotic responses, abnormalities of synapse and neurogenesis in the hippocampus, suggesting that it might has beneficial effects on depression.

Giant Electroresistance in Ferroionic Tunnel Junctions

Oxide-based resistive switching devices, including ferroelectric tunnel junctions and resistance random access memory, are promising candidates for the next-generation non-volatile memory technology. In this work, we propose a ferroionic tunnel junction to realize a giant electroresistance. It functions as a ferroelectric tunnel junction at low resistance state and as a Schottky junction at high resistance state, due to interface engineering through the field-induced migration of oxygen vacancies. An extremely large electroresistance with ON/OFF ratios of 5.1×107 at room temperature and 2.1×109 at 10 K is achieved, using an ultrathin BaTiO3-δ layer as the ferroelectric barrier and a semiconducting Nb-doped SrTiO3 substrate as the bottom electrode. The results point toward an appealing way for the design of high-performance resistive switching devices based on ultrathin oxide heterostructures by ionic controlled interface engineering.

Mediation in the second-order synaptic emulator with conductive atomic force microscopy

Memristors have been extensively studied for synaptic simulation and neuromorphic computation. Instead of focusing on implementing specific synaptic learning rules by carefully engineering external programming parameters, researchers recently have paid more attention to taking advantage of the second-order memristor that is more analogous to biologic synapses and modulated not only by external inputs but also by internal mechanisms. However, experimental evidence is still scarce. Here, we explore a BiMnO3 memristor by applying simple spike forms. The filament evolution dynamics, including processes of forming and spontaneous decay, were directly observed by the conductive atomic force microscopy (c-AFM) technique. We propose that the unique conductance state of the BMO memristor is regulated by the oxygen vacancy (VO) dynamic process. We believe this primary result is helpful to improve understanding of the internal mechanisms of the second-order oxide memristor, which exhibits promising application in building selectors, memories and neuromorphic-computing systems.

Single- and double-gate synaptic transistor with TaO x gate insulator and IGZO channel layer

We demonstrate single- and double-gate synaptic operations of a thin-film transistor (TFT) with double-gate stack consisting of an Al-top-gate/SiO _x /TaO _x /n-IGZO on a SiO₂/n⁺-Si-bottom-gate substrate. This synaptic TFT exhibits a tunable drain current, mimicking synaptic weight modulation in the biological synapse, upon repeatedly applying gate and drain voltages. The drain current modulation features are analog, voltage-polarity dependently reversible, and strong with a dynamic range of multiple orders of magnitude (∼10⁴). These features occur as a consequence of the changes in mobility of the IGZO channel, gate insulator capacitance, and threshold voltage. The drain current modulation responsive to the timing of the voltage application emulates synaptic potentiation, depression, paired-pulse facilitation, and memory transition behaviors depending on the voltage pulse amplitude, width, repetition number, and interval between pulses. The synaptic motions can be realized also by a double-gate operation that separately tunes the channel conductance by top-gate biasing and senses it by bottom-gate biasing. It provides the modulated synaptic weight with a wide level of synaptic weight through the read condition using a bottom-gate stack without read-disturbance. These results verify the potential application of TaO _x /IGZO TFT with single- and double-gate operations to artificial synaptic devices.

Electrochemical metallization cell with solid phase tunable Ge2Sb2Te5 electrolyte

Electrochemical metallization (ECM) cell kinetics are strongly determined by the electrolyte and can hardly be altered after the cell has been fabricated. Solid-state property tunable electrolytes in response to external stimuli are therefore desirable to introduce additional operational degree of freedom to the ECM cells, enabling novel applications such as multistate memory and reconfigurable computation. In this work, we use Ge₂Sb₂Te₅(GST) as the electrolyte material whose solid state is switched from the amorphous(a) to the crystalline(c) phase thermally. Electrical heating too is readily achievable. The resistive switching characteristics of the cells with different GST phases are examined. The magnitude of the high resistance, the SET voltage and the on/off ratio are found to be considerably affected by the solid phase of GST, whereas the magnitude of the low resistance is least affected. Moreover, a transition from volatile to nonvolatile SET switching is only observed for c-GST based cell under prolonged voltage sweep, but not for a-GST based cell. This work provides a springboard for more studies on the manipulation of the ECM cell kinetics by tunable electrolyte and the resulting unprecedented device functionalities.

Salidroside alleviates ischemic brain injury in mice with ischemic stroke through regulating BDNK mediated PI3K/Akt pathway

There is evidence suggesting that inhibition of apoptosis plays a critical role in preventing neurons from damage and even death, after brain ischemia/reperfusion, which shows therapeutic potential for clinical treatment of brain injury. In this study, We preformed MCAO model in C57BL/6J wild-type (BDNK+/+) and BDNK knockout (BDNF-/-) mice respectively, and investigated the neuroprotective effect of Salidroside (Sal) and its underlying mechanisms. The results showed that Sal reversed brain infarct size, reduced cerebral edema, decreased the neurological deficit score and diminished TUNEL positive cells significantly. However, BDNK deficiency inhibited the neuroprotective effect of Sal. In addition, Sal increased cell viability, ameliorated neuron cell injury by decreasing LDH activity, and inhibited cell apoptotic rate. Sal suppressed apoptotic signaling via DNA-binding-dependent and -independent mechanisms. Furthermore, the neuroprotective effect of Sal on BDNK was mediated by PI3K/Akt pathway, which was proved by the use of PI3K knockout (PI3K-/-) mice and siRNA-PI3K. In summary, these data strongly suggested that Sal could be used as an effective neuroprotective agent to protect against ischemic stroke after cerebral I/R injury through regulating BDNK-mediated PI3K/Akt apoptotic pathway in DNA-binding-dependent and -independent manners.

Steep switching devices for low power applications: negative differential capacitance/resistance field effect transistors

Simply including either single ferroelectric oxide layer or threshold selector, we can make conventional field effect transistor to have super steep switching characteristic, i.e., sub-60-mV/decade of subthreshold slope. One of the representative is negative capacitance FET (NCFET), in which a ferroelectric layer is added within its gate stack. The other is phase FET (i.e., negative resistance FET), in which a threshold selector is added to an electrode (e.g., source or drain) of conventional field effect transistor. Although the concept of the aforementioned two devices was presented more or less recently, numerous studies have been published. In this review paper, by reviewing the published studies over the last decade, we shall de-brief and discuss the history and the future perspectives of NCFET/phase FET, respectively. The background, experimental investigation, and future direction for developing the aforementioned two representative steep switching devices (i.e., NCFET and phase FET/negative resistance FET) are to be discussed in detail.