Nonvolatile States Research Articles

High, Multiple, and Nonvolatile Polarizations in Organic-Inorganic Hybrid [(CH3)3(CH2CH2Cl)N]2InCl5·H2O for Memcapacitor.

Dielectrics with high, nonvolatile, and multiple polarizations are required for fabricating memcapacitors that enable high parallelism and low energy consumption in artificial neuromorphic computing systems as artificial synapses. Conventional ferroelectric materials based on displacive and order-disorder types generally have difficulty meeting these requirements due to their low polarization values (∼150 μC/cm2) and persistent electrical hysteresis loops. In this study, we report a novel organic-inorganic hybrid (CETM)2InCl5·H2O (CETM = (CH3)3(CH2CH2Cl)N) exhibiting an intriguing polarization vs electric field (charge vs voltage) "hysteresis loop" and a record-high nonvolatile polarization over 30 000 μC/cm2 at room temperature. The polarization is highly dependent on the period and amplitude of the ac voltage, showing multiple nonvolatile states. Electrochemical impedance spectroscopy, time-dependent current behavior, disparate resistor response in the dehydrated derivative (CETM)2InCl5, and the negative temperature dependence of ionic conductance support that the memcapacitor behavior of (CETM)2InCl5·H2O stems from irreversible long-range migration of protons. First-principles calculations further confirm this and clarify the microscale mechanism of anisotropic polarization response. Our findings may open up a new avenue for developing memcapacitors by harnessing the benefits of ion migration in organic-inorganic hybrids.

Bipolar Resistive Switching in 2D MoSe2 Grown by Atmospheric Pressure Chemical Vapor Deposition.

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) are highly promising nanomaterials for various electronic devices such as field-effect transistors, junction diodes, tunneling devices, and, more recently, memristors. 2D MoSe2 stands out for having high electrical conductivity, charge carrier mobility, and melting point. While these features make it particularly appropriate as a switching layer in memristive devices, reliable and scalable production of large-area 2D MoSe2 still represents a challenge. In this study, we manufacture 2D MoSe2 films by atmospheric-pressure chemical vapor deposition and investigate them on the atomic scale. We selected and transferred MoSe2 bilayer to serve as a switching layer between asymmetric Au-Cu electrodes in miniaturized crossbar vertical memristors. The electrochemical metallization devices showed forming-free, bipolar resistive switching at low voltages, with clearly identifiable nonvolatile states. Other than low-power neuromorphic computing, low switching voltages approaching the range of biological action potentials could unlock hybrid biological interfaces.

Formation of Cluster-Structured Metallic Filaments in Organic Memristors for Wearable Neuromorphic Systems with Bio-Mimetic Synaptic Weight Distributions.

With increasing demand for wearable electronics capable of computing huge data, flexible neuromorphic systems mimicking brain functions have been receiving much attention. Despite considerable efforts in developing practical neural networks utilizing several types of flexible artificial synapses, it is still challenging to develop wearable systems for complex computations due to the difficulties in emulating continuous memory states in a synaptic component. In this study, polymer conductivity is analyzed as a crucial factor in determining the growth dynamics of metallic filaments in organic memristors. Moreover, flexible memristors with bio-mimetic synaptic functions such as linearly tunable weights are demonstrated by engineering the polymer conductivity. In the organic memristor, the cluster-structured filaments are grown within the polymer medium in response to electric stimuli, resulting in gradual resistive switching and stable synaptic plasticity. Additionally, the device exhibits the continuous and numerous non-volatile memory states due to its low leakage current. Furthermore, complex hardware neural networks including ternary logic operators and a noisy image recognitions system are successfully implemented utilizing the developed memristor arrays. This promising concept of creating flexible neural networks with bio-mimetic weight distributions will contribute to the development of a new computing architecture for energy-efficient wearable smart electronics.

Tunable optical power splitter based on directional coupler structure with phase change material Sb2Se3

Traditional optical power splitters (OPSs) have fixed power split ratios, and although some can be tuned with an electro-optic polymer, continuous energy supply increases power consumption. Combining OPS with chalcogenide phase-change materials (PCMs) allows us to tune the power split ratio with a low power owing to the nonvolatility and contrasting optical properties of the PCM. In this paper, a compact 1 × 2 arbitrary ratio OPS with hybrid directional coupler structure with PCM Sb2Se3 is proposed for lower-power and reconfigurable PICs. It demonstrates a continuous change in the power split ratios by changing the crystalline fraction of the Sb2Se3 material covered on coupling waveguide. The non-volatile states of Sb2Se3 enables zero static power of the device. The splitting ratio can be tuned in a broad range from 1 % to 97 % with the excess loss of 0.11 dB at 1550 nm. Such device can be extended to perform a handwritten digit recognition task with the simulated properties to determine the performance of the OPS device in the neuromorphic network. Its recognition accuracy achieves as high as 95 % for the MNIST (Modified National Institute of Standard and Technology) handwritten digits, which is close to that of device with ideal characteristics based on the software computing. The results indicate that the proposed OPS can not only achieve large range tunable power allocation, but also be further applied to large-scale PICs and optical neuromorphic networks.

Diverse long-term potentiation and depression based on multilevel LiSiO x memristor for neuromorphic computing

Memristor-based neuromorphic computing is expected to overcome the bottleneck of von Neumann architecture. An artificial synaptic device with continuous conductance variation is essential for implementing bioinspired neuromorphic systems. In this work, a memristor based on Pt/LiSiO x /TiN structure is developed to emulate an artificial synapse, which shows non-volatile multilevel resistance state memory behavior. Moreover, the high nonlinearity caused by abrupt changes in the set process is optimized by adjusting the initial resistance. 100 levels of continuously modulated conductance states are achieved and the nonlinearity factors are reduced to 1.31. The significant improvement is attributed to the decrease in the Schottky barrier height and the evolution of the conductive filaments. Finally, due to the improved linearity of the long-term potentiation/long-term depression behaviors in LiSiO x memristor, a robust recognition rate (∼94.58%) is achieved for pattern recognition with the modified National Institute of Standards and Technology handwriting database. The Pt/LiSiO x /TiN memristor shows significant potential in high-performance multilevel data storage and neuromorphic computing systems.

Provable security against generic attacks on stream ciphers

Abstract Recent lightweight hardware-based stream cipher designs keep an external non-volatile internal state that is not part of the cipher’s hardware module. The purpose of these so-called small-state ciphers is to keep the size of the hardware and the power consumption low. We propose a random oracle model for stream ciphers. This will allow us to analyse the recent small-state stream cipher designs’ resistance against generic attacks and, in particular, time-memory-data tradeoff attacks. We analyse the conventional construction underlying stream ciphers like Grain and Trivium, constructions continuously using the external non-volatile secret key during keystream generation like Sprout, Plantlet, Fruit, and Atom, constructions continuously using the external non-volatile IV, and constructions using a combination of the IV and the key like DRACO. We show the tightness of all bounds by first presenting the time-memory-data tradeoff attacks on the respective constructions, establishing the upper bound on security, and then presenting the proof of security to establish the lower bound on security. In this work, we extend the theoretical work done by Hamann et al. who introduced the DRACO stream cipher at FSE 2023. We use the same random oracle model as the aforementioned work and apply it to the earlier work by Hamann et al. presented at SAC 2019, which showed security for two of the four constructions we consider in this work. Our model is equivalent but allows for a much simpler proof of security. Furthermore, we provide a proof of security for stream ciphers continuously using the secret key during keystream generation, giving upper and lower bounds for all four generic stream cipher constructions proposed so far.

Switchable ferroelectric photovoltaic response in Sc0.2Al0.8N-based optoelectronic devices

Ferroelectric photovoltaic (FePV) materials are naturally considered a new type of solid-state optoelectronic memory conducting with high storage densities and nonvolatile states. This work investigates ferroelectric properties of Sc0.2Al0.8N thin film with a remnant polarization of 225.2 μC cm−2 and researches the switchable FePV effect and photo-diode characteristics of two-terminal Sc0.2Al0.8N devices. When adopting a two-dimensional material MoTe2 to form a heterostructure, both the photo absorption spectrum and the conductivity of ScAlN-based two-terminal device would be promoted, resulting in the photocurrent at the level of μA cm–2. This work suggests ScAlN can provide a promising FePV implementation for constructing high-performance optoelectronics.

Electrical switching of ferro-rotational order in nanometre-thick 1T-TaS2 crystals.

Hysteretic switching of domain states is a salient characteristic of all ferroic materials and the foundation for their multifunctional applications. Ferro-rotational order is emerging as a type of ferroic order that features structural rotations, but control over state switching remains elusive due to its invariance under both time reversal and spatial inversion. Here we demonstrate electrical switching of ferro-rotational domain states in the charge-density-wave phases of nanometre-thick 1T-TaS2 crystals. Cooling from the high-symmetry phase to the ferro-rotational phase under an external electric field induces domain state switching and domain wall formation, which is realized in a simple two-terminal configuration using a volt-scale bias. Although the electric field does not couple with the order due to symmetry mismatch, it drives domain wall propagation to give rise to reversible, durable and non-volatile isothermal state switching at room temperature. These results offer a route to the manipulation of ferro-rotational order and its nanoelectronic applications.

Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing

Artificial spin ice (ASI) consisting patterned array of nano-magnets with frustrated dipolar interactions offers an excellent platform to study frustrated physics using direct imaging methods. Moreover, ASI often hosts a large number of nearly degenerated and non-volatile spin states that can be used for multi-bit data storage and neuromorphic computing. The realization of the device potential of ASI, however, critically relies on the capability of transport characterization of ASI, which has not been demonstrated so far. Using a tri-axial ASI system as the model system, we demonstrate that transport measurements can be used to distinguish the different spin states of the ASI system. Specifically, by fabricating a tri-layer structure consisting a permalloy base layer, a Cu spacer layer and the tri-axial ASI layer, we clearly resolve different spin states in the tri-axial ASI system using lateral transport measurements. We have further demonstrated that the tri-axial ASI system has all necessary required properties for reservoir computing, including rich spin configurations to store input signals, nonlinear response to input signals, and fading memory effect. The successful transport characterization of ASI opens up the prospect for novel device applications of ASI in multi-bit data storage and neuromorphic computing.

Spin-Topological Electronic Valve in Ni/hBN–Graphene–hBN/Ni Magnetic Junction

A spin-topological electronic valve was discovered in a Ni/hBN–graphene–hBN/Ni magnetic junction to control the in-plane conductance of graphene. By manipulating the mass-gapped Dirac cone (MGDC) of graphene’s topology using the magnetic proximity effect, the spin-topological electronic valve was made possible. The first-principles investigation was conducted to show how the mechanism of graphene’s MGDC is controlled. Twelve stacking configurations for the anti-parallel configuration (APC) and parallel configuration (PC) of the magnetic alignment of Ni slabs were calculated using spin-polarized density functional theory. Three groups can be made based on the relative total energy of the 12 stacking configurations, which corresponds to a van der Waals interaction between hBN and graphene. Each group exhibits distinctive features of graphene’s MGDC. The configuration of the Ni(111) surface state’s interaction with graphene as an evanescent wave significantly impacts how the MGDC behaves. By utilizing the special properties of graphene’s MGDC, which depend on the stacking configuration, a controllable MGDC using mechanical motion was proposed by suggesting a device that can translate the top and bottom Ni(111)/hBN slabs. By changing the stacking configuration from Group I to II and II to III, three different in-plane conductances of graphene were observed, corresponding to three non-volatile memory states. This device provides insight into MJs having three or more non-volatile memory states that cannot be found in conventional MJs.

Edge-Based Two-Dimensional α-In2Se3–MoS2 Ferroelectric Field Effect Device

Heterostructures based on two-dimensional materials offer the possibility to achieve synergistic functionalities, which otherwise remain secluded by their individual counterparts. Herein, ferroelectric polarization switching in α-In2Se3 has been utilized to engineer multilevel nonvolatile conduction states in a partially overlapping α-In2Se3-MoS2-based ferroelectric semiconducting field effect device. In particular, we demonstrate how the intercoupled ferroelectric nature of α-In2Se3 allows to nonvolatilely switch between n-i and n-i-n type junction configurations based on a novel edge state actuation mechanism, paving the way for subnanometric scale nonvolatile device miniaturization. Furthermore, the induced asymmetric polarization enables enhanced photogenerated carriers' separation, resulting in an extremely high photoresponse of ∼1275 A/W in the visible range and strong nonvolatile modulation of the bright A- and B- excitonic emission channels in the overlaying MoS2 monolayer. Our results show significant potential to harness the switchable polarization in partially overlapping α-In2Se3-MoS2 based FeFETs to engineer multimodal, nonvolatile nanoscale electronic and optoelectronic devices.

High-Performance C60 Coupled Ferroelectric Enhanced MoS2 Nonvolatile Memory.

Nonvolatile memory (NVM) devices based on two-dimensional (2D) materials have recently attracted widespread attention due to their high-density integration potential and the ability to be applied in computing-in-memory systems in the post-Moore era. Considering the high current on/off ratio, programmable threshold voltage, nonvolatile multilevel memory state, and extended logic functions, plenty of breakthroughs related to ferroelectric field-effect transistors (FeFETs), one of the most important NVM devices, have been made in the past decade. Among them, FETs coupled with organic ferroelectric films such as P(VDF-TrFE) displayed properties of remarkable robustness, easy preparation, and low cost. However, the dipoles of the P(VDF-TrFE) film cannot be flipped smoothly at low voltage, impeding the further application of organic FeFET. In this paper, we proposed a high-performance FeFET based on monolayer MoS2 coupled with C60 doped ferroelectric copolymer P(VDF-TrFE). The inserted C60 molecules enhanced the alignment of the dipoles effectively at low voltage, allowing the modified device to demonstrate a large memory window (∼16 V), high current on/off ratio (>106), long retention time (>10 000 s), and remarkable endurance under the reduced operating voltage. In addition, the in situ logic application can be realized by constructing facile device interconnection without building complex complementary semiconductor circuits. Our results are expected to pave the way for future low-consumption computing-in-memory applications based on high-quality 2D FeFETs.

Morphology control of volatile resistive switching in La0.67Sr0.33MnO3 thin films on LaAlO3 (001)

The development of in-memory computing hardware components based on different types of resistive materials is an active research area. These materials usually exhibit analog memory states originating from a wide range of physical mechanisms and offer rich prospects for their integration in artificial neural networks. The resistive states are classified as either non-volatile or volatile, and switching occurs when the material properties are triggered by an external stimulus such as temperature, current, voltage, or electric field. The non-volatile resistance state change is typically achieved by the switching layer’s local redox reaction that involves both electronic and ionic movement. In contrast, a volatile change in the resistance state arises due to the transition of the switching layer from an insulator to a metal. Here, we demonstrate volatile resistive switching in twinned LaAlO3 onto which strained thin films of La0.67Sr0.33MnO3 (LSMO) are deposited. An electric current induces phase transition that triggers resistive switching, close to the competing phase transition temperature in LSMO, enabled by the strong correlation between the electronic and magnetic ground states, intrinsic to such materials. This phase transition, characterized by an abrupt resistance change, is typical of a metallic to insulating behavior, due to Joule heating, and manifested as a sharp increase in the voltage with accompanying hysteresis. Our results show that such Joule heating-induced hysteretic resistive switching exhibits different profiles that depend on the substrate texture along the current path, providing an interesting direction toward new multifunctional in-memory computing devices.

Synaptic behavior of Fe3O4-based artificial synapse by electrolyte gating for neuromorphic computing

Neuromorphic computing (NC) is a crucial step toward realizing power-efficient artificial intelligence systems. Hardware implementation of NC is expected to overcome the challenges associated with the conventional von Neumann computer architecture. Synaptic devices that can emulate the rich functionalities of biological synapses are emerging. Out of several approaches, electrolyte-gated synaptic transistors have attracted enormous scientific interest owing to their similar working mechanism. Here, we report a three-terminal electrolyte-gated synaptic transistor based on Fe3O4 thin films, a half-metallic spinel ferrite. We have realized gate-controllable multilevel, non-volatile, and rewritable states for analog computing. Furthermore, we have emulated essential synaptic functions by applying electrical stimulus to the gate terminal of the synaptic device. This work provides a new candidate and a platform for spinel ferrite-based devices for future NC applications.

Hybrid Perovskite‐Based Flexible and Stable Memristor by Complete Solution Process for Neuromorphic Computing

AbstractThe limits of transistor scaling and digital architectures are encouraging research into new electronic materials, devices, and systems to meet growing computing demands. In the realm of artificial intelligence, mimicking brain activity for neuromorphic computing is a promising approach. Herein, Ruddlesden–Popper (RP) perovskite‐based flexible and environmentally stable memristors are presented that achieve on‐demand resistive switching between several nonvolatile states by controlling the number of layers and compliance current (CC). The optimal flexible perovskite device based on n = 5 composition, fabricated by complete solution process and measured under ambient conditions without any encapsulation, shows excellent ON/OFF ratio ≈7 × 103, endurance performance (2500 cycles), and robustness to mechanical flexure up to 5 mm bending radii. The role of the physical/chemical reaction at the perovskite–electrode interface is investigated to reveal the origin of the resistive switching in these devices. The primary probing on synaptic characteristics shows stable learning (potentiation and depression) behavior measured up to 19 000 pulses. The invariant paired pulse facilitation index on flat and 5 mm bending radii demonstrates their feasibility for neuromorphic computing applications. The in‐depth analysis also validates the potential of RP‐based memristor devices for applications that require real‐time synaptic processing under extreme mechanical states such as electronic skins.

Multiple Nonvolatile Skyrmion States in Nanostructured Synthetic Antiferromagnetic Multilayers

Magnetic skyrmions have promising applications as nonvolatile memory for their particlelike and topological-protected features. The recently reported artificial skyrmion has shown a better prospect due to its controllable size and site. Here, we propose and experimentally demonstrate multiple nonvolatile skyrmion states in nanostructured synthetic antiferromagnetic $[\mathrm{Pt}/\mathrm{Co}{]}_{4}/\mathrm{Ru}/[\mathrm{Co}/\mathrm{Pt}{]}_{4}$ multilayers at room temperature. Our magneto-optical Kerr effect measurements of major and minor reversal curves reveal that the skyrmion can exist at zero field with memristive behavior. Using the observed multiple skyrmion states, nonvolatile memory behavior is demonstrated. Our results provide evidence for the concept of skyrmion-based nonvolatile memory, i.e., skyrmion-based memristors and artificial synapses or neurons.

A multilevel electrolyte-gated artificial synapse based on ruthenium-doped cobalt ferrite

Synaptic devices that emulate synchronized memory and processing are considered the core components of neuromorphic computing systems for the low-power implementation of artificial intelligence. In this regard, electrolyte-gated transistors (EGTs) have gained much scientific attention, having a similar working mechanism as the biological synapses. Moreover, compared to a traditional solid-state gate dielectric, the liquid dielectric has the key advantage of inducing extremely large modulation of carrier density while overcoming the problem of electric pinholes, that typically occurs when using large-area films gated through ultra-thin solid dielectrics. Herein we demonstrate a three-terminal synaptic transistor based on ruthenium-doped cobalt ferrite (CRFO) thin films by electrolyte gating. In the CRFO-based EGT, we have obtained multilevel non-volatile conductance states for analog computing and high-density storage. Furthermore, the proposed synaptic transistor exhibited essential synaptic behavior, including spike amplitude-dependent plasticity, spike duration-dependent plasticity, long-term potentiation, and long-term depression successfully by applying electrical pulses. This study can motivate the development of advanced neuromorphic devices that leverage simultaneous modulation of electrical and magnetic properties in the same device and show a new direction to synaptic electronics.

A high linearity and multilevel polymer-based conductive-bridging memristor for artificial synapses.

Conductive-bridging memristors based on a metal ion redox mechanism have good application potential in future neuromorphic computing nanodevices owing to their high resistance switch ratio, fast operating speed, low power consumption and small size. Conductive-bridging memristor devices rely on the redox reaction of metal ions in the dielectric layer to form metal conductive filaments to control the conductance state. However, the migration of metal ions is uncontrollable by the applied bias, resulting in the random generation of conductive filaments, and the conductance state is difficult to accurately control. Herein, we report an organic polymer carboxylated chitosan-based memristor doped with a small amount of the conductive polymer PEDOT:PSS to improve the polymer ionic conductivity and regulate the redox of metal ions. The resulting device exhibits uniform conductive filaments during device operation, more than 100 and non-volatile conductance states with a ∼1 V range, and linear conductance regulation. Moreover, simulation using handwritten digital datasets shows that the recognition accuracy of the carboxylated chitosan-doped PEDOT:PSS memristor array can reach 93%. This work provides a path to facilitate the performance of metal ion-based memristors in artificial synapses.

Optoelectronic synaptic device based on ZnO/HfOx heterojunction for high-performance neuromorphic vision system

Optoelectronic synapses are considered to be important cornerstones in the construction of neuromorphic computing systems because of their low power consumption, high operating speeds, and high scalability. In this work, we demonstrate an optoelectronic synaptic device based on a ZnO/HfOx heterojunction in which optical potentiation/electrical depression behaviors and nonvolatile high current state can be implemented. The heterojunction device exhibits conductance evolution with high linearity. The excellent optoelectronic memristive behavior of the device can be attributed to the interface barrier between ZnO and HfOx, which hinders the recombination of photo-excited electron–hole pairs to increase the carrier lifetime, and realizes the nonvolatile high current state. More importantly, the artificial vision system based on optoelectronic synaptic devices can achieved a high recognition accuracy of 96.1%. Our work provides a feasible pathway toward the development of optoelectronic synaptic devices for use in high-performance neuromorphic vision systems.

Room-temperature valley transistors for low-power neuromorphic computing

Valley pseudospin is an electronic degree of freedom that promises highly efficient information processing applications. However, valley-polarized excitons usually have short pico-second lifetimes, which limits the room-temperature applicability of valleytronic devices. Here, we demonstrate room-temperature valley transistors that operate by generating free carrier valley polarization with a long lifetime. This is achieved by electrostatic manipulation of the non-trivial band topology of the Weyl semiconductor tellurium (Te). We observe valley-polarized diffusion lengths of more than 7 μm and fabricate valley transistors with an ON/OFF ratio of 105 at room temperature. Moreover, we demonstrate an ion insertion/extraction device structure that enables 32 non-volatile memory states with high linearity and symmetry in the Te valley transistor. With ultralow power consumption (~fW valley contribution), we enable the inferring process of artificial neural networks, exhibiting potential for applications in low-power neuromorphic computing.