Memory Characteristics Research Articles

High-Performance Memristive Synapse Based on Space-Charge-Limited Conduction in LiNbO3

Advancing neuromorphic computing technology requires the development of versatile synaptic devices. In this study, we fabricated a high-performance Al/LiNbO3/Pt memristive synapse and emulated various synaptic functions using its primary key operating mechanism, known as oxygen vacancy-mediated valence charge migration (VO-VCM). The voltage-controlled VO-VCM induced space-charge-limited conduction and self-rectifying asymmetric hysteresis behaviors. Moreover, the device exhibited voltage pulse-tunable multi-state memory characteristics because the degree of VO-VCM was dependent on the applied pulse parameters (e.g., polarity, amplitude, width, and interval). As a result, synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and spike time-dependent synaptic plasticity were successfully demonstrated by modulating those pulse parameters. Additionally, simulation studies on hand-written image pattern recognition confirmed that the present device performed with high accuracy, reaching up to 95.2%. The findings suggest that the VO-VCM-based Al/LiNbO3/Pt memristive synapse holds significant promise as a brain-inspired neuromorphic device.

Mucosal Inflammatory Memory in Chronic Rhinosinusitis

Recent advancements in medical management, endoscopic sinus surgery, and biologics have significantly improved outcomes for patients with chronic rhinosinusitis (CRS). However, long-term recurrence is frequently observed following endoscopic sinus surgery, with symptoms worsening after biologics are discontinued. Consequently, refractory or recurrent CRS remains a significant challenge, causing a substantial healthcare burden. In this review, we provide current insights into mucosal inflammatory memory, a potential mechanism leading to CRS recurrence. Given that both immune and non-immune cells in the sinonasal mucosa play critical roles in the pathophysiology of CRS, a deeper understanding of the mechanisms underlying mucosal inflammatory memory in various cellular components of sinonasal tissue could aid in the management of refractory CRS. We describe and discuss the latest knowledge regarding the novel concept of inflammatory memory, including both adaptive immune memory and trained immunity. Additionally, we summarize the pathogenic memory features of the sinonasal mucosa cellular components in the context of CRS.

Doping- and capacitor-less 1T-DRAM cell using reconfigurable feedback mechanism

In this paper, we propose a doping- and capacitor-less 1T-DRAM cell, which achieved virtual doping by leveraging charge plasma and bias-induced electrostatic doping (bias-ED) techniques in a 5 nm-thick intrinsic silicon body, thereby eliminating doping processes. Platinum was in contact with the drain, while aluminum was in contact with the source, enabling virtual doping of the silicon body into ap*-i-n* configuration via the charge-plasma technique. Two coupled polarity gates and one control gate are positioned above the intrinsic channel region. The intrinsic channel region is virtually doped through the bias-ED by applying voltages to the gates, forming potential wells inside the channel. The voltage applied to the two coupled polarity gates determines whether the device operates in thep- orn-channel mode, whereas the control gate governs the flow of charge carriers. Charge carriers are stored and released in the potential wells inside the channel by adjusting the gate, effectively replacing the capacitor. In this device, the placement of polarity gates on either side of the control gate enables the observation of the reconfigurable characteristics. Moreover, the proposed device utilizes a feedback mechanism, enabling excellent memory characteristics such as a high on/off current ratio of ∼109, steep switching behavior of ∼0.2µV dec-1, short write time of 10 ns, long hold retention of over 100 s, and long read retention of over 600 s.

Reservoir Computing System with Diverse Input Patterns in HfAlO-Based Ferroelectric Memristor.

Ferroelectric memristors, particularly those based on hafnia, are gaining attention as potential candidates for neuromorphic computing. These devices offer advantages over perovskite-based ferroelectric memristors owing to their simpler structures, compatibility with complementary metal-oxide semiconductor technology, and low-power consumption characteristics. Additionally, improvements in ferroelectric memristor's performance, such as enhancing tunneling electro resistance (TER) and polarization retention, can be achieved using methods like aluminum doping and insulating film deposition. In this study, we implement a physical reservoir computing (RC) system utilizing the metal-ferroelectric-insulator-semiconductor-structured ferroelectric memristor based on Al-doped HfO2 as an artificial synapse. Specifically, we ensure the universality and diversity of the system by experimentally demonstrating a robust reservoir layer capable of handling various types of input pulses. To utilize the ferroelectric memristor in the reservoir layer of the RC system, we employ partial polarization switching of ferroelectric materials. We measure the retention loss characteristics of the device for pulse amplitude, interval, and width, and quantify the time constant values by fitting them to a stretched exponential function. Additionally, we validate the suitability of the fabricated device as an artificial synapse by mimicking various short-term plasticity functions of biological synapses. Furthermore, we experimentally demonstrate various applications related to learning and memory of the brain, such as image training and Pavlov's experiment, utilizing the short-term memory characteristics of the fabricated device. Lastly, we evaluate the robustness of the RC system under various input conditions by employing the fabricated device as a reservoir layer.

Does enhanced memory of disgust vs. fear images extend to involuntary memory?

ABSTRACT People remember disgusting stimuli better than fearful stimuli, but do disgust’s memory-enhancing effects extend to involuntary memory? This question is important because disgust reactions occur following trauma, and trauma-related involuntary memories are a hallmark of Posttraumatic Stress Disorder (PTSD) symptoms. In two experiments, we presented participants (n = 88 Experiment 1; n = 106 Experiment 2) with disgust, fear, and neutral images during an attention-monitoring task. Participants then completed an undemanding vigilance task, responding any time an image involuntarily came to mind. We measured the frequency and characteristics of these involuntary memories (e.g. emotional intensity) immediately after encoding and over a 24-hour delay (Experiment 2 only). Our main findings were mixed: participants experienced similarly frequent (Experiment 2) – or more (Experiment 1) – disgust as fear involuntary memories. Therefore, when controlling for memory-enhancing confounds (e.g. distinctiveness), in-laboratory disgust memory enhancement does not extend to involuntary memory. Disgust memories were more emotionally intense than fear memories over the 24-hour delay– but not immediately after encoding – suggesting disgust elicits additional consolidation processes to fear. Participants paid more attention towards the disgust images, but the attention did not account for the memory of disgust. In sum, disgust and fear have both similar and distinct cognitive effects.

Conductive Islands Assisted Resistive Switching in Biomimetic Artificial Synapse for Associative Learning and Image Recognition

AbstractSolution‐processed memristor devices hold significant potential for advancing synaptic applications, offering scalable, cost‐effective, and efficient solutions for next‐generation neuromorphic systems. These devices replicate the behavior of biological synapses with their gradual and continuous changes in resistance, making them promising for neuromorphic computing and artificial neural networks. However, understanding the temporal characteristics and cumulative effect of successive pulses on the devices is essential for emulating the dynamics of biological synapses. This study proposes a hybrid materials‐based conductive islands‐assisted resistive switching (CIARS) in a solution‐processed active layer consisting of thermally exfoliated graphitic carbon nitride (g‐C3N4 abbreviated as CN) nanosheets embedded with silver nanoparticles (Ag NPs). The fabricated devices demonstrate repeatable and bipolar memory features with a lower operating voltage (≤0.5 V), emulating the frequency and amplitude‐dependent synaptic plasticities. The threshold switching occurs due to the formation of conduction filaments (CFs) of silver ion (Ag+), whereas the analog behavior results from the voltage pulse‐dependent modulation of Schottky barrier height combined with metallic CFs formation. The experimental findings demonstrate the efficacy of the CIARS mechanism within the material platform, highlighting its potential for applications in associative learning, Morse code detection, and image recognition.

The massed-spaced learning effect in non-neural human cells

The massed-spaced effect is a hallmark feature of memory formation. We now demonstrate this effect in two separate non-neural, immortalized cell lines stably expressing a short-lived luciferase reporter controlled by a CREB-dependent promoter. We emulate training using repeated pulses of forskolin and/or phorbol ester, and, as a proxy for memory, measure luciferase expression at various points after training. Four spaced pulses of either agonist elicit stronger and more sustained luciferase expression than a single “massed” pulse. Spaced pulses also result in stronger and more sustained activation of molecular factors critical for memory formation, ERK and CREB, and inhibition of ERK or CREB blocks the massed-spaced effect. Our findings show that canonical features of memory do not necessarily depend on neural circuitry, but can be embedded in the dynamics of signaling cascades conserved across different cell types.

Nitrogen-doped carbon quantum dot-decorated In2O3 synaptic transistors for neuromorphic computing

Nitrogen-doped carbon quantum dots (N-CQDs) are promising materials for electronic devices due to their variable bandgap and structural stability. Here, we integrate N-CQDs into In2O3 synaptic transistors with electrolyte gating, resulting in a hybrid structure. The surface functional groups and defects of N-CQDs empower the charge trapping mechanism, permitting controlled conduction and charge regulation, which are crucial for emulating linear and symmetric artificial synaptic devices. Devices incorporating N-CQDs demonstrate enhanced stability and memory characteristics, low energy consumption, consistent retention, and a significant hysteresis window across multiple voltage cycles. Finally, the study emulates biological synapses and cognitive functions, achieving an energy consumption of 10 fJ per synaptic event and a pattern recognition accuracy of 91.2% on the MNIST dataset in hardware neural networks. This work demonstrates the potential of well-manipulating charge trapping in N-CQDs to develop high-performance, nonvolatile synaptic devices.

First principle atomistic modelling of magnetic tunnel junction (MTJ) to enhance its tunnel magneto-resistance (TMR) ratio

Abstract This paper investigates the impact of electrode materials on the Tunnel Magneto-Resistance (TMR) ratio of Magnetic Tunnel Junction (MTJ) device. Four different structures of MTJ have been simulated by using cobalt (Co), Nickel (Ni), Iron (Fe) and two alloy materials of nickel-iron (NiFe) and cobalt-iron (CoFe). These materials have been used as ferromagnetic electrodes. Mulliken population and transmission spectrum observed in both parallel and antiparallel configurations of these devices to understand the spin transport properties and Tunnel Magneto-Resistance (TMR) ratio has been estimated. The first principal study was performed based on density function theory (DFT) and Non-equilibrium Green’s Function (NEGF) computational methods using the QuantumATK simulation tool to study properties such as band structure, the density of states (DOS), Spin Transfer Torque (STT), I-V characteristics and TMR. This study explores how different electrode materials affect the Tunnel Magneto-Resistance (TMR) ratio in Magnetic Tunnel Junction (MTJ) devices. With these results, it is observed that cobalt-based MTJ devices (that is Co-MgO-Fe and CoFe-MgO-CoFe) exhibit higher TMR ratio as compared to Nickel- and Iron-based MTJ devices (that is NiFe-MgO-NiFe and Ni-MgO-Fe). As Cobalt has a high spin polarization this property makes it suitable for use in spintronics devices like MTJs, where the manipulation of electron spins is essential for data storage and information processing. These findings can be employed to improve the performance characteristics of the MTJ-based Random Access Memory (MRAM) in the field of spintronics.

Treating Cognition in Schizophrenia: A Whole Lifespan Perspective.

Background/Objectives: Cognitive impairment is a core feature of schizophrenia, affecting attention, memory, and executive function and contributing significantly to the burden of the disorder. These deficits often begin before the onset of psychotic symptoms and persist throughout life, making their treatment essential for improving outcomes and functionality. This work aims to explore the impact of these impairments at different life stages and the interventions that have been developed to mitigate their effects. Methods: This narrative review examined literature searching for different approaches to treat cognitive impairments in schizophrenia across the lifespan. Results: Cognitive alterations appear before psychosis onset, suggesting a window for primary prevention. Then, a period of relative stability with a slight decline gives the period to secondary and eventually tertiary prevention for more than two decades. Finally, another window for tertiary prevention occurs from the third decade of illness until the later stages of the illness, when a progression in cognitive decline could be accelerated in some cases. Cognitive remediation and physical exercise are evidence-based interventions that should be provided to all patients with disabilities. Conclusions: Treating cognition throughout the whole lifespan is crucial for improving functional outcomes. It is necessary to consider the need for personalized, stage-specific strategies to enhance cognitive function and functioning in patients.

A Deep Learning Approach for Enhanced Real-Time Prediction of Winter Road Surface Temperatures in High-Altitude Mountain Areas

Low temperatures and icing in winter are significant factors that severely affect highway safety and traffic mobility. To enhance the precision and reliability of real-time winter road surface temperature (RST) prediction, a short-term prediction model is developed that harnesses both feature selection and deep learning. Leveraging meteorological data from a mountain highway in Yunnan, China, the key environmental variables affecting road surface temperature were first extracted using a random forest (RF) model for feature selection. These features were then combined with RST data to construct multiple groups of input variable combinations for the prediction model. A short-term prediction model with a 10-minute update frequency was built using a long short-term memory neural network (LSTM), namely RF-LSTM. The best input variable combination and preset parameters for the prediction model were determined through comparative testing with prevalent machine learning models, and the transferability of the prediction model was verified. The results showed that the best input variable combination for the RF-LSTM prediction model was road surface temperature and air temperature. The model recognised that the short-term RST was affected by long and short-term memory characteristics within a two-hour timeframe. When compared to the RF model, backpropagation (BP) neural network model and the standard LSTM model, the proposed model reduces prediction errors by 59.15%, 31.10% and 20.26%, respectively, while the prediction accuracy is 99.13% within an error margin of ±0.5℃. On the verification dataset, the proposed model maintains its time transferability with an average prediction absolute error of 0.0478. In all, the proposed model not only achieves a higher level of precision in real-time RST predictions but also ensures a more consistent and reliable performance under the challenging conditions of high altitude and mountainous terrain, offering enhanced support for traffic safety and road maintenance decision-making.

Integrated model of cerebellal supervised learning and basal ganglia’s reinforcement learning for mobile robot behavioral decision-making

Behavioral decision-making in unknown environments of mobile robots is a crucial research topic in robotics. Inspired by the working mechanism of different brain regions in mammals, this paper designed a new hybrid model integrating the functions of cerebellum and basal ganglia by simulating the memory replay of hippocampus, so as to realize the autonomous behavioral decision-making of robot in unknown environments. A reinforcement learning module based on Actor-Critic framework and a developmental network module are used to simulate the functions of the basal ganglia and cerebellum, respectively. Considering the different functions of D1 and D2 dopamine receptors in basal ganglia, an Actor network module with separate learning of positive and negative rewards is designed for the basal ganglia to realize efficient exploration of the environments by the agent. According to the characteristics of biological memory, a physiological memory priority index is designed for hippocampus memory replay, which improves the offline learning efficiency of cerebellum. The integrated model enables dynamic switching between decisions made by cerebellum and basal ganglia based on the agent’s cognitive level with respect to the environment. Finally, the effectiveness of the proposed model is verified through experiments on agent navigation in both simulation and real environments, as well as through performance comparison experiments with other learning algorithms.

PKCδ is an activator of neuronal mitochondrial metabolism that mediates the spacing effect on memory consolidation.

Relevance-based selectivity and high energy cost are two distinct features of long-term memory (LTM) formation that warrant its default inhibition. Spaced repetition of learning is a highly conserved cognitive mechanism that can lift this inhibition. Here, we questioned how the spacing effect integrates experience selection and energy efficiency at the cellular and molecular levels. We showed in Drosophila that spaced training triggers LTM formation by extending over several hours an increased mitochondrial metabolic activity in neurons of the associative memory center, the mushroom bodies (MBs). We found that this effect is mediated by PKCδ, a member of the so-called 'novel PKC' family of enzymes, which uncovers the critical function of PKCδ in neurons as a regulator of mitochondrial metabolism for LTM. Additionally, PKCδ activation and translocation to mitochondria result from LTM-specific dopamine signaling on MB neurons. By bridging experience-dependent neuronal circuit activity with metabolic modulation of memory-encoding neurons, PKCδ signaling binds the cognitive and metabolic constraints underlying LTM formation into a unified gating mechanism.

PKCδ is an activator of neuronal mitochondrial metabolism that mediates the spacing effect on memory consolidation

Relevance-based selectivity and high energy cost are two distinct features of long-term memory (LTM) formation that warrant its default inhibition. Spaced repetition of learning is a highly conserved cognitive mechanism that can lift this inhibition. Here, we questioned how the spacing effect integrates experience selection and energy efficiency at the cellular and molecular levels. We showed in Drosophila that spaced training triggers LTM formation by extending over several hours an increased mitochondrial metabolic activity in neurons of the associative memory center, the mushroom bodies (MBs). We found that this effect is mediated by PKCδ, a member of the so-called ‘novel PKC’ family of enzymes, which uncovers the critical function of PKCδ in neurons as a regulator of mitochondrial metabolism for LTM. Additionally, PKCδ activation and translocation to mitochondria result from LTM-specific dopamine signaling on MB neurons. By bridging experience-dependent neuronal circuit activity with metabolic modulation of memory-encoding neurons, PKCδ signaling binds the cognitive and metabolic constraints underlying LTM formation into a unified gating mechanism.

Tailoring the Tunneling‐Effect‐Boosted Interfacial Charge Trapping via Effective Conjugation Length

AbstractHighly efficient charge injection and charge trapping stability of the tunneling layer are of desirable and practical importance to transistor memory applications. However, both of which can be contradictory to the nature properties of its material. It is herein demonstrated that lowering the energy mismatch of the tunneling layer by employing a longer conjugation length of the polymer can improve the charge injection efficiency, albeit the trapped charges will be easily diminished and finally losing its memory characteristics, and vice versa. To further elaborate and verify this concept, both materials are blended with distinct nature of properties as the tunneling layer in pentacene‐based transistor devices. As the results, the device using 300 nm SiO2 with optimum blending ratio displays a broad memory window of ≈77.6 V which is superior to the non‐blended tunneling layer carrying appropriate energy levels and band gap energy, not to mention, revealing fast operation time (≈1 s), low driving voltage (≈20 V), long retention (>104 s), and high switching stability over 50 cycles with on–off ratio of >104. Most importantly, this finding shed insight into the design of tunneling materials for advancing organic transistor memory technologies based on tunneling‐effect‐boosted interfacial charge trapping.

Application of Smart Polymer Materialsarts

With the wide application of polymer materials, the performance of ordinary polymer materials gradually cannot meet the shortcomings of use in special environments, such as limited strength, insufficient wear resistance, poor stability and so on. And can not adjust and repair according to changes in the environment. Therefore, the demand for intelligent polymer materials has gradually expanded, and has been applied to a variety of fields after research. This paper introduces the information and application of three kinds of intelligent polymer materials: targeting, shape memory and self-healing. Targeting is mainly used in cancer or other diseases that require precise drug delivery; The characteristics of shape memory can be applied to plaster and surgical sutures in the medical field, and can also be applied to the performance upgrade of traditional manufacturing industry. Self-healing can be used to improve the service life of composite materials by using different methods according to the requirements of different material use scenarios.

Physical Reservoir Computing Using Tellurium-Based Gate-Tunable Artificial Photonic Synapses.

We report tellurium (Te) thin-film-based artificial photonic synapses and their application to physical reservoir computing (PRC). The Te-based artificial photonic synapses were fabricated by using sputtered Te thin films and spray-coated MXene (Ti3C2) electrodes. A thorough investigation of the field-dependent persistent photoconductivity (PPC) of the Te channel revealed that the relaxation speed of the transient photocurrent depended on the gate bias. Utilizing the PPC property, the Te device served as an excellent photonic synapse under light pulse stimulus, exhibiting multiple synaptic characteristics such as excitatory postsynaptic current and paired-pulse facilitation, as well as highly linear potentiation-depression characteristics; a simulation-based study further confirmed the effectiveness of the device. Most importantly, by exploiting the nonlinear and fading memory characteristics of the Te photonic synapse, we demonstrate two advanced examples of PRC. In classifying handwritten digits, our system carried out successful digit recognition without binarization or another simplification process with reduced computational cost compared to conventional systems. To solve second-order nonlinear equations, we introduce the strategy of utilizing historical nodes. The combination of historical nodes and the gate-tunable responses of the photonic synapses, which provide an enriched reservoir state, yielded excellent prediction accuracy. Overall, this work will offer an understanding of Te-based optoelectronic devices and their synergetic integration with neuromorphic devices and PRC.

Analog reservoir computing via ferroelectric mixed phase boundary transistors

Analog reservoir computing (ARC) systems have attracted attention owing to their efficiency in processing temporal information. However, the distinct functionalities of the system components pose challenges for hardware implementation. Herein, we report a fully integrated ARC system that leverages material versatility of the ferroelectric-to-mixed phase boundary (MPB) hafnium zirconium oxides integrated onto indium–gallium–zinc oxide thin-film transistors (TFTs). MPB-based TFTs (MPBTFTs) with nonlinear short-term memory characteristics are utilized for physical reservoirs and artificial neuron, while nonvolatile ferroelectric TFTs mimic synaptic behavior for readout networks. Furthermore, double-gate configuration of MPBTFTs enhances reservoir state differentiation and state expansion for physical reservoir and processes both excitatory and inhibitory pulses for neuronal functionality with minimal hardware burden. The seamless integration of ARC components on a single wafer executes complex real-world time-series predictions with a low normalized root mean squared error of 0.28. The material-device co-optimization proposed in this study paves the way for the development of area- and energy-efficient ARC systems.

Flexible TiO2-WO3−x hybrid memristor with enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing

Tungsten oxide (WO3)-based memristors show promising applications in neuromorphic computing. However, single-layer WO3 memristors suffer from issues such as weak memory performance and nonlinear conductance variations. In this work, a functional layer based on the hybrids of WO3−x and TiO2 is proposed for constructing flexible memristors featuring outstanding synaptic characteristics. Applying diverse electrical stimulations to the memristor enables a range of synaptic functions, elucidating its conduction mechanism through the conductive filament model. The incorporation of TiO2 not only enhances the memristor’s memory characteristics but makes its conductance more linear, symmetrical and uniform during the long-term changes. Furthermore, in view of the enhanced device performance by TiO2 doping, the potential of this device for simple behavioral simulation and processing of complex computing problems is explored. The “learning-forgetting-relearning” characteristics and device integrability are visually demonstrated. Applying the device to a convolutional neural network, the recognition accuracy of MNIST handwritten digits reaches 98.7%.

Improvement of charge trapping memory performance by modulating band alignment with oxygen plasma

Metal-oxide charge trapping memory (CTM) integration into amorphous and organic flexible devices encounters challenges due to high-temperature treatment. Our approach enhances memory performance via room-temperature oxygen plasma treatment, subtly adjusting surface band alignment without changing the original material structure and surface roughness. Infiltration of oxygen plasma induces band alignment bending, creating a barrier for charge trapping. The device with oxygen plasma treatment exhibits an impressive 19.06 V memory window and a charge trapping density of 3.58 × 1013/cm2. In comparison, the memory window of untreated device only has 5.56 V, demonstrating that oxygen plasma treatment significantly improves memory characteristics. The charge retention rate exhibits outstanding stability, potentially reaching 94% after a decade. It should be noted that careful control during plasma treatment is crucial to maintaining optimal memory effects. This facile, efficient technique, applicable to various oxide layers, offers a way for future advancements in metal-oxide CTM technology.