Despite their great synaptic potential, the trade-off between programming speed and energy consumption of electrochemical random-access memory (ECRAM) devices are major hindrance to their incorporation into practical applications. In this work, we experimentally study the main limiting factor for high-speed programming of ECRAMs, the ionic current in the gate stack. We use two-terminal structures composed of LiCoO2/Li3PO4/amorphous-Si to represent the ECRAM gate stack (reservoir/electrolyte/channel). We perform electrical characterization including impedance spectroscopy (small-signal) and large-signal transient measurements across nine orders of magnitude in the time domain. We find that at the sub-microseconds range, the current is governed by the energy barrier for Li+ ions at the electrolyte interfaces. After a period of ∼1 μs, ionic migration through the ∼80 nm electrolyte layer dictates the current. At ∼50 μs, the ionic double layer at the interface is fully charged and the gate current drops by several orders of magnitude, indicating that the Li3PO4/Si interface is saturated, and the measured current is dominated by the electronic leakage component. Furthermore, we evaluate ECRAM performance under various pulse parameters. Our predictions show that an aggressively scaled (atomically thin) channel having a low carrier density of ∼1011 cm−2 can be programmed at ∼nanosecond using a gate current of ∼100 A/cm2.

Ionic–electronic dynamics in an electrochemical gate stack toward high-speed artificial synapses

Oscillatory neural networks (ONNs) directly emulate signal communication between biological neurons in the human brain by encoding the data in phase domain, enabling energy-efficient associative memory. An oscillation neuron (ON) element that generates continuous voltage spikes with a specific frequency needs to be designed for hardware implementation. Thus, we systematically investigate the role of the ON in edge detection in ONN systems through simulation. First, a threshold switch is experimentally fabricated for the ON using niobium oxide (NbO $_{x}{)}$ material, and voltage oscillation is realized in HSPICE and MATLAB. Subsequently, we examine how each voltage oscillation in a coupled-ON system, in which two NbOx-based ONs are connected with a coupling resistance, is mutually synchronized. Simulation results reveal that a small (or large) coupling resistance strengthens the in-phase (or out-of-phase) synchronization of the two independent oscillations. The synchronized phase expressed in the form of period is found to be adjusted by tuning various components. As two clearly distinguishable phases are obtained, ONN systems, where multiple ONs are cross-coupled, can be utilized for edge detection during image processing. Patterns are trained using Hebbian learning rule in an ONN system comprising ten ONs, and a feature of the handwritten digit image is accurately extracted. Moreover, the feasibility of accelerating the edge detection step is further explored through various engineering approaches to change the characteristics of the NbOx-based ONs.

Understanding Rhythmic Synchronization of Oscillatory Neural Networks Based on NbOx Artificial Neurons for Edge Detection

Resistance random access memory (RRAM) is considered one of the most promising candidates for the next-generation nonvolatile memory (NVM). Previous research has also reported the pinch-off effect on the one-transistor-one-resistor (1T1R) device structure induced by transistor operation. Therefore, a thorough understanding of the relationship between transistor and RRAM operations can help reach the most out of the 1T1R device. In this work, a complete investigation is made regarding different operation parameters affecting the variations of high resistance state (HRS) within the 1T1R device. A body-RESET method has also been proposed to mitigate the limitation from the transistor to achieve better HRS results without affecting the low resistance state (LRS) for the 1T1R device. Finally, the discussion on the difference of HRS and LRS of the 1T1R structure is made based on both p-type and n-type transistors, which also provides a possible means regarding the 1T1R device operation to increase memory without further degradation of the device performance.

Improving High Resistance State in One-Transistor-One-Resistor (1T1R) Structure Resistance Random Access Memory With a Body-Biased Method

Oscillatory neural network (ONN)-based classification of clustered data relies on frequency synchronization to injected signals representing input data, showing a more efficient structure than a conventional deep neural network. A frequency tunable oscillator is a core component of the network, requiring energy-efficient, and area-scalable characteristics for large-scale hardware implementation. From a hardware viewpoint, insulator-metal transition (IMT) device-based oscillators are attractive owing to their simple structure and low power consumption. Furthermore, by introducing non-volatile analog memory, non-volatile frequency programmability can be obtained. However, the required device characteristics of the oscillator for high performance of coupled oscillator have not been identified. In this article, we investigated the effect of device parameters of IMT oscillator with non-volatile analog memory on coupled oscillators network for classification of clustered data. We confirmed that linear conductance response with identical pulses is crucial to accurate training. In addition, considering dispersed clustered inputs, a wide synchronization window achieved by controlling the hold voltage of the IMT shows resilient classification. As an oscillator that satisfies the requirements, we evaluated the NbO2-based IMT oscillator with non-volatile Li-based electrochemical random access memory (Li-ECRAM). Finally, we demonstrated a coupled oscillator network for classifying spoken vowels, achieving an accuracy of 85%, higher than that of a ring oscillator-based system. Our results show that an NbO2-based oscillator with Li-ECRAM has the potential for an area-scalable and energy-efficient network with high performance.

https://www.frontiersin.org/articles/10.3389/fnins.2022.939687/pdf

Linear Frequency Modulation of NbO2-Based Nanoscale Oscillator With Li-Based Electrochemical Random Access Memory for Compact Coupled Oscillatory Neural Network

In the age of the Internet of Things, the proliferation of edge devices has resulted in a significant increase in personal information that is susceptible to theft and counterfeiting at various stages of data communication. As a result, substantial attention has been focused on hardware (HW) security elements, such as the true random number generator and physical unclonable function. With the recent surge in research and development of emerging memristors, which exploit the inherent variability of these devices, there has been a notable increase in studies on HW security. Particularly, volatile threshold switch (TS) devices, which exhibit insulator/metal characteristics below/above a certain threshold voltage, show great promise as security devices due to their lower power consumption and higher cycling endurance compared to nonvolatile memory devices. Despite the promising attributes and increasing demand for TS devices for HW security, there remains a lack of a comprehensive overview covering various TS devices and their potential contributions to HW privacy. To address this gap, this review provides an encompassing analysis of different types of TS devices and their performance in HW security literature, providing insight into current limitations and the future prospects of HW security primitives based on TS devices.

Volatile threshold switching devices for hardware security primitives: Exploiting intrinsic variability as an entropy source

In this letter, we propose a compact frequency programmable oscillator with an NbO2-based insulator-metal transition (IMT) device and three-terminal Li-based electro-chemical RAM (Li-ECRAM) for coupled oscillators-based temporal pattern recognition system. Owing to the non-volatility, multilevel characteristics, and linear conductance modulation of Li-ECRAM, our proposed oscillator exhibits a large number of programmable frequencies (~45) and high controllability by applying pulses to the Li-ECRAM to attain the target frequency. Furthermore, we demonstrated injection locking phenomenon in our proposed oscillators, which can be utilized for the frequency detection of the injected signal. Finally, we simulated four-coupled oscillators system for the frequency classification of the input temporal signal with multiple frequencies and amplitude noise. These results demonstrate the feasibility of a temporal pattern recognition system composed of our proposed compact frequency-programmable oscillators.

Nonvolatile Frequency-Programmable Oscillator With NbO2 and Li-Based Electro-Chemical Random Access Memory for Coupled Oscillators-Based Temporal Pattern Recognition System

Conductance variations of resistive random-access memory (RRAM) are significant challenges that hinder the accurate inference of neural network (NN) hardware. In this study, we exploit the read noise of the RRAM as an active computational enabler for implementing probabilistic NN. As electrical characteristics of RRAM are directly related to the properties of conductive filament (CF), we statistically explore read current of TiOx-based RRAM with different forming conditions and explain the results by linking the CF model. In addition, an array mapping scheme to transfer weights to one transistor-one RRAM (1T1R) array is experimentally demonstrated. Through NN simulations, we verify that the probabilistic NN shows promising results on nonlinear classification problem avoiding overconfidence compared with deterministic NN.

Exploiting Read Current Noise of TiOx Resistive Memory by Controlling Forming Conditions for Probabilistic Neural Network Hardware

Neuromorphic computing has gained a considerable research interest due to its potential in realizing highly efficient parallel computations. However, the existing neuromorphic architectures face various drawbacks. In this study, we present an integrate-and-fire (I&F) neuron using a Li-based electrochemical random access memory (Li-ECRAM) to achieve exceptional area efficiency and low-power neuromorphic computing. The proposed Li-ECRAM neuron employs a significantly reduced number of transistors when compared to other novel nonvolatile memory-based I&F neurons due to linear current integration characteristics and a high linear conductance response to the input current. As the integration-type Li-ECRAM is linear, it eliminates the requirement of a nonlinear compensating circuit. Therefore, a Li-ECRAM-based neuron has a simple structure comprising Li-ECRAM, reset transistor, inverter, and pulse generator. Furthermore, we also evaluate the operation speed and energy consumption of the proposed neuron, demonstrating the potential for high-speed and low-power operation. The proposed neuron can be applied in large-scale neuromorphic hardware applications due to the scalability and low energy consumption of Li-ECRAM.

Integrate-and-Fire Neuron With Li-Based Electrochemical Random Access Memory Using Native Linear Current Integration Characteristics

In this study, stochastic resonance (SR) exploits the inherent stochastic characteristics of the OTS threshold voltage to enhance the inference performance of neural networks. First, the threshold switching of the OTS device is characterized, and a signal detection using an OTS device is proposed. Next, we investigate the impact of stochasticity in the threshold voltage on detecting weak signals in the SR system. Finally, by evaluating the inference performance of the artificial neural network, we confirm that the inherent stochasticity can effectively restore the degraded MNIST image in poor visibility conditions in the OTS device. As a result, the recognition accuracy was improved from 10.28% to 95.78% when the stochasticity characteristic was reflected. These results show that stochasticity in the device can improve system performance.

Effect of Stochastic Resonance on Classification Accuracy of Neural Networks Utilizing Inherent Stochasticity in Threshold Voltage of Ovonic Threshold Switching Device

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chuljun Lee

Author Tools

Chat about Author

Papers

Nonvolatile Frequency-Programmable Oscillator With NbO2 and Li-Based Electro-Chemical Random Access Memory for Coupled Oscillators-Based Temporal Pattern Recognition System

Exploiting Read Current Noise of TiOx Resistive Memory by Controlling Forming Conditions for Probabilistic Neural Network Hardware

Integrate-and-Fire Neuron With Li-Based Electrochemical Random Access Memory Using Native Linear Current Integration Characteristics

Effect of Stochastic Resonance on Classification Accuracy of Neural Networks Utilizing Inherent Stochasticity in Threshold Voltage of Ovonic Threshold Switching Device