Characteristics Of Short-term Memory Research Articles

Volatile memory characteristics of CMOS-compatible HZO ferroelectric layer for reservoir computing

Recently, ferroelectric memory utilizing hafnium oxide has emerged as an attractive option compared to existing memory technologies, primarily due to its scalability and energy-efficient advantages. Among them, hafnium zirconium oxide (HZO) is examined for its short-term memory characteristics to achieve a reservoir computing system known to exhibit remarkable polarization properties, being able to switch between distinct polarization states under the influence of an electric field. These unique properties are of utmost importance in ferroelectric memory applications, where they play a pivotal role in the storage and retrieval of binary data. In this study, we identify and experiment with the electrical characteristics of a ferroelectric tunnel junction (FTJ) device with a metal-ferroelectric-semiconductor (MFS) structure using TiN as the top electrode and HZO as the ferroelectric layer. Moreover, we assess the performance of the device by evaluating its maximum 2Pr (remnant polarization) and tunneling electro resistance (TER) values in different conditions of cell area. Furthermore, we analyze and show short-term memory (STM) characteristics and synaptic properties with 5 cycles of potentiation and depression under conditions of stable dynamic range by coordinating identical and incremental pulses. In the case of incremental pulses (> 95%), the MNIST pattern recognition accuracy is higher than in the case of identical pulses (> 94%). Through a sequence of procedures, the synaptic characteristics of FTJs are confirmed to assess their suitability for use as an artificial synaptic device.

InGaZnO-based synaptic transistor with embedded ZnO charge-trapping layer for reservoir computing

In this study, we design an IGZO/ZnO/IGZO-based synaptic transistor to implement robust reservoir computing. Short-term memory characteristics are achieved using the charge trapping and detrapping effects of the ZnO layer. We verify excellent cell-to-cell and cycle-to-cycle uniformity regarding the memory characteristics of the device. Moreover, various synaptic behaviors, including short-term potentiation, short-term depression (STD), excitatory postsynaptic currents (EPSC), and paired-pulse facilitation (PPF) are emulated to check the suitability of neuromorphic properties. Finally, reservoir computing trained on the modified National Institute of Standards and Technology database dataset is presented for temporal data learning. As a physical reservoir, the device can achieve 16 different using 4 bits depending on the applied pulse stream. The results include a confusion matrix covering all recognition scenarios, with an average recognition accuracy of 93.87%, closely approaching the theoretical recognition accuracy of 94.1%. This study sheds light on a computational framework for physical reservoir computing by reducing the training cost.

IGZO/SnOx-based dynamic memristor with fading memory effect for reservoir computing

We investigate a synaptic device with short-term memory characteristics using IGZO/SnOx as the switching layer. The thickness and components of each layer are analyzed by using x-ray photoelectron spectroscopy and transmission electron microscopy. The memristor exhibits analog resistive switching and a volatile feature with current decay over time. Moreover, through ten cycles of potentiation and depression, we demonstrate stable conductance modulation, leading to high-accuracy Modified National Institute of Standards and Technology pattern recognition. We effectively emulate the learning system of a biological synapse, including paired-pulse facilitation, spiking-amplitude-dependent plasticity, and spiking-rate-dependent plasticity (SRDP) by pulse trains. Ultimately, 4-bit reservoir computing divided into 16 states is incarnated using a pulse stream considering short-term memory plasticity and decay properties.

CHARACTERISTICS OF SHORT-TERM MEMORY OF MEDICAL STUDENTS AT CAN THO UNIVERSITY OF MEDICINE AND PHARMACY

Background: Short-term memory is the foundation of long-term memory, which greatly influences cognitive activities, especially in the field of education. Medical students contribute to showing an overview of the characteristics of short-term memory of medical students in particular and students in general in a developing country like Vietnam. Objectives: Surveying the characteristics of visual and auditory short-term memory through numerical data and image data in medical students. Materials and methods: Using a cross-sectional descriptive design method on 160 medical students studying at Can Tho University of Medicine and Pharmacy. The study includes surveys of visual and auditory short-term memory using two types of numerical and image information, using a questionnaire built based on the Nachaive method in the form of online through a website app and collected the results via email. Results: The median results of visual short-term memory (VSTM) in numerical and image data are 11 and 12 respectively. The VSTM figure in numerical data shows that the highest score is 12 (f=46). The VSTM figure in image data records that the score 12 has a dominant frequency (f=87). The median results of auditory short-term memory (ASTM) in numerical and image forms are 10 and 9 respectively. The ASTM figure in numerical data shows that the highest score is 12 (f=32). The ASTM figure in image data notes that score 9 has the highest frequency (f=37). Conclusions: Students of Can Tho University of Medicine and Pharmacy have visual short-term memory that is superior to auditory short-term memory in terms of memorization. The results of visual short-term memory in image data are better than visual short-term memory in numerical data. Conversely, the results of auditory short-term memory in numerical data are better than auditory short-term memory in image data.

Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing.

The von Neumann architecture has faced challenges requiring high-fulfillment levels due to the performance gap between its processor and memory. Among the numerous resistive-switching random-access memories, the properties of hexagonal boron nitride (BN) have been extensively reported, but those of amorphous BN have been insufficiently explored for memory applications. Herein, we fabricated a Pt/BN/TiN device utilizing the resistive switching mechanism to achieve synaptic characteristics in a neuromorphic system. The switching mechanism is investigated based on the I-V curves. Utilizing these characteristics, we optimize the potentiation and depression to mimic the biological synapse. In artificial neural networks, high-recognition rates are achieved using linear conductance updates in a memristor device. The short-term memory characteristics are investigated in depression by controlling the conductance level and time interval.

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing

Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain.

Artificial Synapse Emulated by Indium Tin Oxide/SiN/TaN Resistive Switching Device for Neuromorphic System.

In this paper, we fabricate an ITO/SiN/TaN memristor device and analyze its electrical characteristics for a neuromorphic system. The device structure and chemical properties are investigated using transmission electron microscopy and X-ray photoelectron spectroscopy. Uniform bipolar switching is achieved through DC sweep under a compliance current of 5 mA. Also, the analog reset phenomenon is observed by modulating the reset voltage for long-term memory. Additionally, short-term memory characteristics are obtained by controlling the strength of the pulse response. Finally, bio-inspired synaptic characteristics are emulated using Hebbian learning rules such as spike-rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP). As a result, we believe that the coexistence of short-term and long-term memories in the ITO/SiN/TaN device can provide flexibility in device design in future neuromorphic applications.

Piezoelectric MEMS-based physical reservoir computing system without time-delayed feedback

Abstract In this study, a physical reservoir computing system, a hardware-implemented neural network, was demonstrated using a piezoelectric MEMS resonator. The transient response of the resonator was used to incorporate short-term memory characteristics into the system, eliminating commonly used time-delayed feedback. In addition, the short-term memory characteristics were improved by introducing a delayed signal using a capacitance-resistor series circuit. A Pb(Zr,Ti)O3-based piezoelectric MEMS resonator with a resonance frequency of 193.2 Hz was employed as an actual node, and computational performance was evaluated using a virtual node method. Benchmark tests using random binary data indicated that the system exhibited short-term memory characteristics for two previous data and nonlinearity. To obtain this level of performance, the data bit period must be longer than the time constant of the transient response of the resonator. These outcomes suggest the feasibility of MEMS sensors with machine-learning capability.

Nano-crystalline ZnO memristor for neuromorphic computing: Resistive switching and conductance modulation

In this work, a nano-crystalline (NC) ZnO-based memristor was fabricated to investigate the short-term memory characteristics for reservoir computing systems. The crystalline structure of the ZnO film was confirmed through transmission electron microscopy (TEM) and X-ray diffraction pattern (XRD), while X-ray photoelectron spectroscopy (XPS) confirmed the chemical and bonding states of each element. The NC-ZnO-based memristor exhibited remarkable endurance, enduring more than 200 DC cycles, and had a high to low resistance (RH/RL) ratio of 102. Furthermore, it displayed long data retention of 104 s and consistent resistive switching (RS) with restricted variation in the set and reset voltage, showing its excellent performance characteristics. By controlling the pulse amplitude and the time interval between pulses, it was possible to effectively replicate the key features of short-term synaptic plasticity, including potentiation, depression, and paired-pulse depression, through conductance modulation. An artificial neural network (ANN) simulation achieved a pattern recognition accuracy of approximately 90.1% for a 28 × 28-pixel image after 100 training epochs. Based on this extensive study, NC-ZnO-based memristor exhibits immense potential as a crucial element in constructing high-performance neuromorphic computing systems.

Short-term memory characteristics in n-type-ZnO/p-type-NiO heterojunction synaptic devices for reservoir computing

We investigated the short-term memory characteristics of n-type-ZnO/p-type-NiO heterojunction-based memristor devices to achieve a reservoir computing system. The deposited thickness and chemical properties of each layer of the device were verified by transmission electron microscopy and energy-dispersive X-ray spectroscopy. The TiN/ZnO/NiO/Pt device exhibited the resistive switching behaviors of a typical p-n junction-based RRAM with rectification characteristics. The self-rectification characteristics of the device were attributed to the diode effect of the p-n junction and the redox reaction of TiON. Thus, stable and controllable multistate analog memory was obtained using gradual bipolar resistive switching during successive sweeping cycles. Moreover, excellent potentiation and depression were achieved with a stable dynamic range over 10 cycles by coordinated identical pulses. High MNIST pattern recognition accuracy (>94%) was obtained through a three-layer neural network (784 × 224 × 10); short-term memory dynamics were observed using DC current read sweep by controlling the delay time after the set process and conductance decay, such as paired-pulse facilitation, by varying the pulse interval. Finally, we constructed a reservoircomputing system with 16 states (4-bit) distinguished by varying pulse streams ranging from [0000] to [1111] using the short-term memory effects of the TiN/ZnO/NiO/Pt device.

Short-Term Memory Characteristics of IGZO-Based Three-Terminal Devices.

A three-terminal synaptic transistor enables more accurate controllability over the conductance compared with traditional two-terminal synaptic devices for the synaptic devices in hardware-oriented neuromorphic systems. In this work, we fabricated IGZO-based three-terminal devices comprising HfAlOx and CeOx layers to demonstrate the synaptic operations. The chemical compositions and thicknesses of the devices were verified by transmission electron microscopy and energy dispersive spectroscopy in cooperation. The excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), short-term potentiation (STP), and short-term depression (STD) of the synaptic devices were realized for the short-term memory behaviors. The IGZO-based three-terminal synaptic transistor could thus be controlled appropriately by the amplitude, width, and interval time of the pulses for implementing the neuromorphic systems.

CMOS-compatible self-aligned 3D memristive elements for reservoir computing systems

Neuromorphic capabilities of a self-aligned complementary metal-oxide-semiconductor compatible W/WOx/HfO2/Ru cell in a 3D vertical memristive structure were investigated. We show that the device exhibits nonfilamentary forming-free multilevel resistive switching with gradual resistance change. In addition, the poor retention of a low resistance state allows integration of these structures in architectures that require short-term memory characteristics such as reservoir computing systems. The ability of the device to rely on the temporal sequence of the stream was tested with the digit recognition task. Since a WOx layer was obtained by thermal oxidization and HfO2 and Ru layers were grown by atomic layer deposition methods, the device is suitable for high-density systems with high connectivity within a neural network.

Enhanced Short-Term Memory Plasticity of WOx-Based Memristors by Inserting AlOx Thin Layer.

ITO/WOx/TaN and ITO/WOx/AlOx/TaN memory cells were fabricated as a neuromorphic device that is compatible with CMOS. They are suitable for the information age, which requires a large amount of data as next-generation memory. The device with a thin AlOx layer deposited by atomic layer deposition (ALD) has different electrical characteristics from the device without an AlOx layer. The low current is achieved by inserting an ultra-thin AlOx layer between the switching layer and the bottom electrode due to the tunneling barrier effect. Moreover, the short-term memory characteristics in bilayer devices are enhanced. The WOx/AlOx device returns to the HRS without a separate reset process or energy consumption. The amount of gradual current reduction could be controlled by interval time. In addition, it is possible to maintain LRS for a longer time by forming it to implement long-term memory.

Synaptic plasticity features and neuromorphic system simulation in AlN-based memristor devices

In this paper, we show various memory characteristics of the Ag/AlN/TiN devices for neuromorphic systems. We verified the thickness and the components of the device stack by transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). We investigated the long-term memory (LTM) characteristics, and short-term memory (STM) characteristics can be determined by compliance current (CC). It shows LTM characteristics when CC is high and STM characteristics when CC is low. I-V curves for each characteristic were investigated, and potentiation and depression for LTM characteristics. The switching and conduction mechanisms of Ni/Ag/AlN/TiN devices are studied using the schematic drawing of the conducting filament and the energy band diagram, including the work function, electron affinity, and bandgap energy of each layer. The linearity of potentiation and depression was compared for an identical pulse and an incremental pulse. Finally, we investigated Modified National Institute of Standards and Technology (MNIST) pattern accuracy depending on the linearity of potentiation and depression.

Dynamic Nonlinear Behavior of Ionic Liquid-Based Reservoir Computing Devices.

Herein, a physical reservoir device that uses faradaic currents generated by redox reactions of metal ions in ionic liquids was developed. Synthetic time-series data consisting of randomly arranged binary number sequences (“1” and “0”) were applied as isosceles-triangular voltage pulses with positive and negative voltage heights, respectively, and the effects of the faradaic current on short-term memory and parity-check task accuracies were verified. The current signal for the first half of the triangular voltage-pulse period, which contained a much higher faradaic current component compared to that of the second half of the triangular voltage-pulse period, enabled higher short-term memory task accuracy. Furthermore, when parity-check tasks were performed using a faradaic current generated by asymmetric triangular voltage-pulse levels of 1 and 0, the parity-check task accuracy was approximately eight times higher than that of the symmetric triangular voltage pulse in terms of the correlation coefficient between the output signal and target data. These results demonstrate the advantage of the faradaic current on both the short-term memory characteristics and nonlinear conversion capabilities and are expected to provide guidance for designing and controlling various physical reservoir devices that utilize electrochemical reactions.

(Digital Presentation) Reservoir Computing Device Using Faradaic Current of Electroactive Species in Ionic Liquids

Physical Reservoir Computing (PRC) has recently attracted significant attention as a computational method suitable for the edge AI computing, which requires both the high performance of information processing and energy conservative operation. There are two standard methods for evaluating PRC performance: the short-term memory (STM) task for the memory capacity and the parity check (PC) task for the nonlinear conversion capacity. We have developed PR device utilizing Faradaic currents generated by the redox reaction of metal ions in ionic liquids (ILs) and the impact of metal ions in ILs on the STM characteristics has been evaluated by comparing the RC device using metal-ion doped IL with that using non-doped (pure) IL [1]. In this study, we investigated the effect of Faradaic current on the STM and PC tasks by extracting the Faradaic current from the output signal when the triangular shaped input voltage pulse was applied to the PR device. It was found out that the peak shaped Faradaic current in the output signal improves not only the memory capacity but also the nonlinear conversion capability.A reservoir device with a transverse Pt/SiO2/Pt structure was prepared (Fig. 1) and solvated IL, Cu(Tf2N)2-Glyme(G3)=1:1 [2], was provided between the Pt electrodes as a reaction field where electrochemical reaction of Cu actively takes place. In order to prevent unnecessary copper deposition on the top surface of the Pt electrode, all areas other than the Pt electrode tip was covered with SiO2. As a result, a structure that allows deposition only between the terminals was realized. Furthermore, to prevent the migration of the IL by the application of an electric field, an IL pool surrounded by a resist wall was formed by patterning a spin-coated photoresist AZ5214E with a thickness of 2 µm. Au/Ti (100/10 nm) was deposited as the contact pad. The device characteristics were evaluated by cyclic voltammetry. In addition, a response of the device to the triangular pulses was investigated by using the B1530A WGFMU (waveform generator / fast measurement unit). STM and PC tasks were used to evaluate the time series data processing ability.In the present study, an artificial time-series data consisting of randomly connected binary data (0 and 1) was input to one of the Pt electrodes as the triangular shaped voltage pulse stream, while the other electrode was grounded. The positive and negative voltages were defined as 2bit data, ‘1’ and ‘0’, respectively. As shown in Fig. 2, triangular voltage pulse streams shown by the blue line were input to the reservoir device, and output current shown by red line was observed. Current peak appears when the polarity of the input signal is switched from positive to negative and vice versa (black arrow), but the peak intensity decreases when application of voltage pulses with the same polarity are continuously repeated. In addition, Cu deposition on the Pt electrode was observed. These results indicate that the origin of the peak is the Faradaic current generated by the electroactive species near the electrode. We divided output current data into two parts; the first half (yellow region), the latter half (green region). The first half corresponds to the rising part of the triangular pulse and involves the Faradaic current. On the other hand, the latter half corresponds to the descending part of the triangular pulse and is more featureless compared with the first half. We found that the accuracy of STM task was much higher when the first half was used. This tendency was also confirmed for the PC task. In PRC based on the concept of virtual nodes using a single physical device [3], the output signal complexity is considered to be related to the multidimensional transformation capability, which is one of indispensable property for PRC [4]. The present results suggest that the redox reaction of electroactive species in the IL increases the complexity of the output signal and improves the ability to extract time-series features.We thank Mr. Hiroshi Sato for supporting device fabrication processes. A part of this work was supported by "Nanotechnology Platform Program", Grant Number JPMXPF21NM0006.[1] D. Sato et al., MEMRISYS 2021, 4A-7 (2021).[2] H. Yamaoka et al., Chem. Lett., 46, 1832-1835 (2017).[3] L. Appeltant et al., Nat. Commun., 2, 468 (2011).[4] L. Appeltant et al., Sci. Rep., 4, 3629 (2014). Figure 1

Energy efficient short-term memory characteristics in Ag/SnOx/TiN RRAM for neuromorphic system

In this work, we demonstrate the short-term memory effects of an Ag/SnOx/TiN memristor device using the spontaneous reset process for a neuromorphic system. The thickness and chemical properties of the SnOx layer are investigated via transmission electron microscopy and X-ray photoemission spectroscopy. The non-volatile and volatile memory properties are determined by the forming process, which is verified by the retention test. The conductance change increases as the interval between the pulses decrease because of the reduction of the spontaneous current decay. Moreover, paired-pulse facilitation (PPF) characteristics are demonstrated, where the PFF index decreases with an increase in the pulse interval. We first investigated potentiation with the application of set pulses and depression without reset pulses is examined for the neuromorphic system. With this pulse, we successfully implemented high-performance reservoir computing that has a range from [0000] to [1111] using the short-term memory characteristics of the Ag/SnOx/TiN device.

Implementation of reservoir computing using volatile WOx-based memristor

In this study, we investigate a Ni/WOx/ITO-glass memristor device to verify short-term memory characteristics for reservoir computing systems. We verify the chemical and material compositions of each layer using transmission electron microscopy (TEM) image and X-ray photoelectron spectroscopy (XPS). The device has a characteristic that the current decreases with time, but shows a reverse current decay phenomenon. In addition, potentiation and depression data are obtained through modulated pulses and measurement methods. Based on this result, meaningful pattern recognition accuracy is obtained. Also, it is proved that the gradual conductance modulation can be controlled through pulse amplitude and time interval between the pulses. Finally, reservoir computing is realized based on short-term characteristics of the device. All 16 states of 4 bits have been implemented, and it is proved that the changed state can be classified using a simple learning algorithm after reading it with pulses. We also propose to make the system to consume low power.

Tonal structures benefit short-term memory for real music: Evidence from non-musicians and individuals with congenital amusia

Congenital amusia is a neurodevelopmental disorder of music processing, which includes impaired pitch memory, associated to abnormalities in the right fronto-temporal network. Previous research has shown that tonal structures (as defined by the Western musical system) improve short-term memory performance for short tone sequences (in comparison to atonal versions) in non-musician listeners, but the tonal structures only benefited response times in amusic individuals. We here tested the potential benefit of tonal structures for short-term memory with more complex musical material. Congenital amusics and their matched non-musician controls were required to indicate whether two excerpts were the same or different. Results confirmed impaired performance of amusic individuals in this short-term memory task. However, most importantly, both groups of participants showed better memory performance for tonal material than for atonal material. These results revealed that even amusics’ impaired short-term memory for pitch shows classical characteristics of short-term memory, that is the mnemonic benefit of structure in the to-be-memorized material. The findings show that amusic individuals have acquired some implicit knowledge of regularities of their culture, allowing for implicit processing of tonal structures, which benefits to memory even for complex material.

Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links.

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.