Nonassociative learning implementation by a single memristor-based multi-terminal synaptic device

An Artificial LiSiOx Nociceptor with Neural Blockade and Self-Protection Abilities

An artificial nociceptor, as a critical and special bionic receptor, plays a key role in a bioelectronic device that detects stimuli and provides warnings. However, fully exploiting bioelectronic applications remains a major challenge due to the lack of the methods of implementing basic nociceptor functions and nociceptive blockade in a single device. In this work, we developed a Pt/LiSiOx/TiN artificial nociceptor. It had excellent stability under the 104 endurance test with pulse stimuli and exhibited a significant threshold current of 1 mA with 1 V pulse stimuli. Other functions such as relaxation, inadaptation, and sensitization were all realized in a single device. Also, the pain blockade function was first achieved in this nociceptor with over a 25% blocking degree, suggesting a self-protection function. More importantly, an obvious depression was activated by a stimulus over 1.6 V due to the cooperative effects of both lithium ions and oxygen ions in LiSiOx and the dramatic accumulation of Joule heat. The conducting channel ruptured partially under sequential potentiation, thus achieving nociceptive blockade, besides basic functions in one single nociceptor, which was rarely reported. These results provided important guidelines for constructing high-performance memristor-based artificial nociceptors and opened up an alternative approach to the realization of bioelectronic systems for artificial intelligence.

Silver Nanowire Networks with Moisture-Enhanced Learning Ability

The human brain possesses a remarkable ability to memorize information with the assistance of a specific external environment. Therefore, mimicking the human brain's environment-enhanced learning abilities in artificial electronic devices is essential but remains a considerable challenge. Here, a network of Ag nanowires with a moisture-enhanced learning ability, which can mimic long-term potentiation (LTP) synaptic plasticity at an ultralow operating voltage as low as 0.01 V, is presented. To realize a moisture-enhanced learning ability and to adjust the aggregations of Ag ions, we introduced a thin polyvinylpyrrolidone (PVP) coating layer with moisture-sensitive properties to the surfaces of the Ag nanowires of Ag ions. That Ag nanowire network was shown to exhibit, in response to the humidity of its operating environment, different learning speeds during the LTP process. In high-humidity environments, the synaptic plasticity was significantly strengthened with a higher learning speed compared with that in relatively low-humidity environments. Based on experimental and simulation results, we attribute this enhancement to the higher electric mobility of the Ag ions in the water-absorbed PVP layer. Finally, we demonstrated by simulation that the moisture-enhanced synaptic plasticity enabled the device to adjust connection weights and delivery modes based on various input patterns. The recognition rate of a handwritten data set reached 94.5% with fewer epochs in a high-humidity environment. This work shows the feasibility of building our electronic device to achieve artificial adaptive learning abilities.

Nano t-Se Peninsulas Embedded in Natively Oxidized 2D TiSe2 Enable Uniform and Fast Memristive Switching

Memristive devices, regardless of their potential applications in memory and computing scenarios, still suffer from large cycle-to-cycle and device-to-device variations due to the stochastic growth of conductive filaments (CFs). In this work, we fabricated a crossbar memristor using the 2D TiSe₂ material and then oxidized it into TiO₂ in the atmosphere at a moderate temperature. Such a mild oxidation approach fails to evaporate all Se into the air, and after further annealing using thermal or electrical stimulations, the remnant Se atoms gather near the interfaces and grow into nanosized crystals with relatively high conductivity. The resulting peninsula-shaped nanocrystals distort the electric field, forcing CFs to grow on them, which could largely confine the location and length of CFs. As a result, this two-terminal TiSe₂/TiO₂/TiSe₂ device exhibits excellent resistive switching performance with a fairly low threshold voltage (V_set < 0.8 V, V_reset > 0.55 V) and high cycle-to-cycle consistency, enabling resistive switching at narrow operating variations, e.g., 500 ± 48 and 845 ± 39 mV. Our work offers a new approach to minimize the cycle-to-cycle stochasticity of the memristive device, paving the way for its applications in data storage and brain-inspired computing.

Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence

The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.

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.

Multi-Level Switching of Al-Doped HfO2 RRAM with a Single Voltage Amplitude Set Pulse

In this paper, the resistive switching characteristics in a Ti/HfO2: Al/Pt sandwiched structure are investigated for gradual conductance tuning inherent functions. The variation in conductance of the device under different amplitudes and voltage pulse widths is studied. At the same time, it was found that the variation in switching parameters in resistive random-access memory (RRAM) under impulse response is impacted by the initial conductance states. The device was brought to a preset resistance value range by energizing a single voltage amplitude pulse with a different number of periodicities. This is an efficient and simple programming algorithm to simulate the strength change observed in biological synapses. It exhibited an on/off of about 100, an endurance of over 500 cycles, and a lifetime (at 85 °C) of around 105 s. This multi-level switching two-terminal device can be used for neuromorphic applications to simulate the gradual potentiation (increasing conductance) and inhibition (decreasing conductance) in an artificial synapse.

Emulation of biphasic plasticity in retinal electrical synapses for light-adaptive pattern pre-processing

Electrical synapses provide rapid, bidirectional communication in nervous systems, accomplishing tasks distinct from and complementary to chemical synapses. Here, we demonstrate an artificial electrical synapse based on second-order conductance transition (SOCT) in an Ag-based memristor for the first time. High-resolution transmission electron microscopy indicates that SOCT is mediated by the virtual silver electrode. Besides the conventional chemical synaptic behaviors, the biphasic plasticity of electrical synapses is well emulated by integrating the device with a photosensitive element to form an optical pre-processing unit (OPU), which contributes to the retinal neural circuitry and is adaptive to ambient illumination. By synergizing the OPU and spiking neural network (SNN), adaptive pattern recognition tasks are accomplished under different light and noise settings. This work not only contributes to the further completion of synaptic behaviour for hardware-level neuromorphic computing, but also potentially enables image pre-processing with light adaptation and noise suppression for adaptive visual recognition.

Memristive Circuit Implementation of Biological Nonassociative Learning Mechanism and Its Applications

Biological nonassociative learning is one of the simplest forms of unsupervised learning in animals and can be categorized into habituation and sensitization according to mechanism. This paper proposes a memristive circuit that is based on nonassociative learning and can adapt to repeated inputs, reduce power consumption (habituation), and be sensitive to harmful inputs (sensitization). The circuit includes 1) synapse module, 2) neuron module, 3) feedback module. The first module mainly consists of memristors representing synapse weights that vary with corresponding inputs. Memristance is automatically reduced when a harmful stimulus is input, and climbs at the input interval according to the feedback input when repeated stimuli are input. The second module produces spiking voltage when the total input is above the given threshold. The third module can provide feedback voltage according to the frequency and quantity of input stimuli. Simulation results show that the proposed circuit can generate output signals with biological nonassociative learning characteristics, with varying amplitudes depending on the characteristics of input signals. When the frequency and quantity of the input stimuli are high, the degree of habituation and sensitization intensifies. The proposed circuit has good robustness; can reduce the influence of noise, circuit parasitics and circuit aging during nonassociative learning; and simulate the afterimages caused by visual fatigue for application in automatic exposure compensation.

Nonlinear Characteristics of Complementary Resistive Switching in HfAlOx-Based Memristor for High-Density Cross-Point Array Structure

In this work, we present the nonlinear current–voltage (I–V) characteristics of a complementary resistive switching (CRS)-like curve from a HfAlOx-based memristor, used to implement a high-density cross-point array. A Pt/HfAlOx/TiN device has lower on-current and larger selectivity compared to Pt/HfO2/TiN or Pt/Al2O3/TiN devices. It has been shown that the on-current and first reset peak current after the forming process are crucial in obtaining a CRS-like curve. We demonstrate transient CRS-like characteristics with high nonlinearity under pulse response for practical applications. Finally, after finding the optimal conditions for high selectivity, the calculated read margin proves that a Pt/HfAlOx/TiN device with a CRS-like curve is most suitable for use in a high-density cross-point array. Our results suggest that the built-in selector properties in a Pt/HfAlOx/TiN single layer device offer considerable potential in terms of the simplicity of the processes involved in the cross-point structure.

Heterogeneous stimuli induced nonassociative learning behavior in ZnO nanowire memristor

Nonassociative learning is a biologically essential and evolutionarily adaptive behavior in organisms. The bionic simulation of nonassociative learning based on electronic devices is essential to the neuromorphic computing. In this work, nonassociative learning is mimicked by a ZnO nanowire memristor without any other peripheral control circuit. The memristor demonstrates habituation and sensitization behaviors at the electrical and optical stimuli. Typical network-level parametric characteristics of habituation in neuroscience are realized in the memristor. When the heterogeneous stimuli are applied coincidentally, sensitization pulse could be identified by the exceptional response current. The results show that the natural selection rules could be simulated by the current single memristor. A possible mechanism based on the trapping states and adsorption of oxygen at the interface of Au/ZnO is proposed. The implementation of nonassociative learning in a single memristor device paves the way for building neuromorphic systems by simple electronic devices.

Dual-Gated MoS2 Neuristor for Neuromorphic Computing

The field of neuromorphic computing systems has been through enormous progress in recent years, whereas some issues are still remaining to be solved. One of the biggest challenges in neuromorphic circuit designing is the lack of a robust device with functions comparable to or even better than the metal-oxide-semiconductor field-effect transistor (MOSFET) used in traditional integrated circuits. In this work, we demonstrated a MoS₂ neuristor using a dual-gate transistor structure. An ionic top gate is designed to control the migration of ions, while an electronic back gate is used to control electronic migration. By applying different driving signals, the MoS₂ neuristor can be programmed as a neuron, a synapse, or an n-type MOSFET, which can be seen as a fundamental building block in the neuromorphic circuit design. The MoS₂ neuristor provides viable solutions for future reconfigurable neuromorphic systems and can be a promising candidate for future neuromorphic computing.

Evolution map of the memristor: from pure capacitive state to resistive switching state

Memristors possess great application prospects in terabit nonvolatile storage devices, memory-in-logic algorithmic chips and bio-inspired artificial neural network systems. However, "what is the origin state of the memristor?" has remained an unanswered question for half a century. While many applications rely on the memristor, its origin state is becoming a fundamental issue. Herein, we reveal a new state, the pure capacitance state (PCS), which occurs before the memristor is triggered, and the origin state of the memristor can be verified in the memory cells through controlling the ambience parameters. Discovery of the PCS, a missing earlier stage of the memristor, completes the whole evolution map of the memristor from the very beginning to the final developed state.

Gate Tuning of Synaptic Functions Based on Oxygen Vacancy Distribution Control in Four-Terminal TiO2-x Memristive Devices

Recent developments in artificial intelligence technology has facilitated advances in neuromorphic computing. Electrical elements mimicking the role of synapses are crucial building blocks for neuromorphic computers. Although various types of two-terminal memristive devices have emerged in the mainstream of synaptic devices, a hetero-synaptic artificial synapse, i.e., one with modulatable plasticity induced by multiple connections of synapses, is intriguing. Here, a synaptic device with tunable synapse plasticity is presented that is based on a simple four-terminal rutile TiO_2−x single-crystal memristor. In this device, the oxygen vacancy distribution in TiO_2−x and the associated bulk carrier conduction can be used to control the resistance of the device. There are two diagonally arranged pairs of electrodes with distinct functions: one for the read/write operation, the other for the gating operation. This arrangement enables precise control of the oxygen vacancy distribution. Microscopic analysis of the Ti valence states in the device reveals the origin of resistance switching phenomena to be an electrically driven redistribution of oxygen vacancies with no changes in crystal structure. Tuning protocols for the write and the gate voltage applications enable high precision control of resistance, or synaptic plasticity, paving the way for the manipulation of learning efficiency through neuromorphic devices.

Light Driven Active Transition of Switching Modes in Homogeneous Oxides/Graphene Heterostructure

Depending on the mobile species involved in the resistive switching process, redox random access memories and conductive bridge random access memories are widely studied with distinct switching mechanisms. Although the two resistance switching types have faithfully proved to be electrochemically linked in metal oxide-based memristive devices, the corresponding photo-induced transition has not yet been realized. Here, a photo-induced transition through the integration of a graphene layer into a titanium oxide-based memory device is demonstrated. Coupled with Raman mapping and an electron energy loss spectroscopy technique, the photo-induced interaction at the heterostructure of graphene/titanium oxide are considered to dominate the transition process. Moreover, a negative differential resistance effect is observed by controlling the applied voltage, which can be credited to the saturation of trap centers (oxygen vacancies) and the increase of interfacial barrier at the graphene/titanium oxide heterojunction.

A Multi-level Memristor Based on Al-Doped HfO2 Thin Film

Non-volatile memory (NVM) will play a very important role in the next-generation digital technologies, including the Internet of things. The metal-oxide memristors, especially based on HfO₂, have been favored by lots of researchers because of its simple structure, high integration, fast operation speed, low power consumption, and high compatibility with advanced (complementary metal oxide silicon) CMOS technologies. In this paper, a 20-level stable resistance states Al-doped HfO₂-based memristor is presented. Its cycles endurance, data retention time, and resistance ratio are larger than 10³, > 10⁴ s, and > 10, respectively.

Configurable multi-state non-volatile memory behaviors in Ti3C2 nanosheets

MXenes have drawn considerable attention in both academia and industry due to their attractive properties, such as a combination of metallic conductivity and surface hydrophilicity. However, to the best of our knowledge, the potential use of MXenes in non-volatile resistive random access memories (RRAMs) has rarely been reported. In this paper, we first demonstrated a RRAM device with MXene (Ti3C2) as the active component. The Ti3C2-based RRAM exhibited typical bipolar switching behavior, long retention characteristics, low SET voltage, good mechanical stability and excellent reliability. By adjusting different compliance currents in the SET process, multi-state information storage was achieved. The charge trapping assisting hopping process is considered to be the main mechanism of resistive switching for this fabricated Ti3C2-based RRAM, which was verified by conductive atomic force microscopy (C-AFM) and Kelvin probe force microscopy (KPFM). Moreover, this flexible Ti3C2-based RRAM, with good mechanical stability and long retention properties, was successfully fabricated on a plastic substrate. Ti3C2-based RRAMs may open the door to additional applications and functionalities, with high potential for application in flexible electronics.

Synaptic plasticity, metaplasticity and memory effects in hybrid organic-inorganic bismuth-based materials

Since the discovery of memristors, their application in computing systems utilizing multivalued logic and a neuromimetic approach is of great interest. A thin film device made of methylammonium bismuth iodide exhibits a wide variety of neuromorphic effects simultaneously, and is thus able to mimic synaptic behaviour and learning phenomena. Standard learning protocols, such as spike-timing dependent plasticity and spike-rate dependent plasticity might be further modulated via metaplasticity in order to amplify or alter changes in the synaptic weight. Moreover, transfer of information from short-term to long-term memory is observed. These effects show that the diversity of functions of memristive devices can be strongly affected by the pre-treatment of the sample. Modulation of the resistive switching amplitude is of great importance for the application of memristive elements in computational applications, as additional sub-states might be utilized in multi-valued logic systems and metaplasticity and memory consolidation will contribute to the development of more efficient bioinspired computational schemes.

A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors

Physically transient electronic devices that can disappear on demand have great application prospects in the field of information security, implantable biomedical systems, and environment friendly electronics. On the other hand, the memristor-based artificial synapse is a promising candidate for new generation neuromorphic computing systems in artificial intelligence applications. Therefore, a physically transient synapse based on memristors is highly desirable for security neuromorphic computing and bio-integrated systems. Here, this is the first presentation of fully degradable biomimetic synaptic devices based on a W/MgO/ZnO/Mo memristor on a silk protein substrate, which show remarkable information storage and synaptic characteristics including long-term potentiation (LTP), long-term depression (LTD) and spike timing dependent plasticity (STDP) behaviors. Moreover, to emulate the apoptotic process of biological neurons, the transient synapse devices can be dissolved completely in phosphate-buffered saline solution (PBS) or deionized (DI) water in 7 min. This work opens the route to security neuromorphic computing for smart security and defense electronic systems, as well as for neuro-medicine and implantable electronic systems.

Artificial Shape Perception Retina Network Based on Tunable Memristive Neurons

Retina shows an extremely high signal processing efficiency because of its specific signal processing strategy which called computing in sensor. In retina, photoreceptor cells encode light signals into spikes and ganglion cells finish the shape perception process. In order to realize the neuromorphic vision sensor, the one-transistor-one-memristor (1T1M) structure which formed by one memristor and one MOSFET in serial is used to construct photoreceptor cell and ganglion cell. The voltage changes between two terminals of memristor and MOSFET can mimic the changes of membrane potential caused by spikes and illumination respectively. In this paper, the tunable memristive neurons with 1T1M structures are built. According to the concept of receptive field of ganglion cells (GCs) in the retina, the artificial shape perception retina network is constructed with these memristive neurons. The final results show that the artificial retina can extract shape information from the image and transfer it into spike frequency realizing the function of computing in sensor.

Customized binary and multi-level HfO2-x-based memristors tuned by oxidation conditions

The memristor is a promising candidate for the next generation non-volatile memory, especially based on HfO2-x, given its compatibility with advanced CMOS technologies. Although various resistive transitions were reported independently, customized binary and multi-level memristors in unified HfO2-x material have not been studied. Here we report Pt/HfO2-x/Ti memristors with double memristive modes, forming-free and low operation voltage, which were tuned by oxidation conditions of HfO2-x films. As O/Hf ratios of HfO2-x films increase, the forming voltages, SET voltages, and Roff/Ron windows increase regularly while their resistive transitions undergo from gradually to sharply in I/V sweep. Two memristors with typical resistive transitions were studied to customize binary and multi-level memristive modes, respectively. For binary mode, high-speed switching with 103 pulses (10 ns) and retention test at 85 °C (>104 s) were achieved. For multi-level mode, the 12-levels stable resistance states were confirmed by ongoing multi-window switching (ranging from 10 ns to 1 μs and completing 10 cycles of each pulse). Our customized binary and multi-level HfO2-x-based memristors show high-speed switching, multi-level storage and excellent stability, which can be separately applied to logic computing and neuromorphic computing, further suitable for in-memory computing chip when deposition atmosphere may be fine-tuned.