scholarly journals A Bioinspired Stretchable Sensory-Neuromorphic System

2020 ◽  
Author(s):  
Sun Hong Kim ◽  
Geun Woo Baek ◽  
Jiyong Yoon ◽  
Seunghwan Seo ◽  
Donghyo Hahm ◽  
...  
Keyword(s):  
Structural Reliability ◽  
Light Emitting Diode ◽  
Random Access ◽  
Areal Density ◽  
Resistive Random Access Memory ◽  
Stretchable Electronics ◽  
Capacitive Pressure Sensor ◽  
Wearable Electronics ◽  
Neuromorphic System ◽  
Tortoise Beetle

Abstract Conventional stretchable electronics entailing the adoption of a wavy design, a neutral mechanical plane, and a conformal contact between abiotic and biotic interfaces have shown diverse skin-interfaced applications. Despite such remarkable progress, there have been challenged to be evolved to intelligent skin prosthetics due to the absence of the monolithic integration of neuromorphic constituents into individual sensing and actuating components. Herein, we demonstrate a golden tortoise beetle-inspired stretchable sensory-neuromorphic system comprising an artificial mechanoreceptor, an artificial synapse, and an epidermal photonic actuator as three biomimetic functionalities that correspond to a stretchable capacitive pressure sensor, a resistive random-access memory, and a quantum dot light-emitting diode, respectively. This system features a rigid-island structure interconnected with a sinter-free printable conductor (stretchability ~ 160%, conductivity ~ 18,550 S/cm), which allows one to improve both areal density and structural reliability while avoiding the thermal degradation of heat-sensitive stretchable electronic components. Moreover, even in the skin deformation range, the system accurately recognizes various patterned stimuli via an artificial neural network with training/inferencing functions. Our new bioinspired system is therefore expected to be an important step toward the implementation of intelligent wearable electronics.

scholarly journals Improved Synaptic Device Properties of HfAlOx Dielectric on Highly Doped Silicon Substrate by Partial Reset Process

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 772
Author(s):  
Seunghyun Kim ◽  
Osung Kwon ◽  
Hojeong Ryu ◽  
Sungjun Kim
Keyword(s):  
Resistive Switching ◽  
Silicon Substrate ◽  
Photoelectron Spectroscopy ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Alloy Type ◽  
Switching Type ◽  
Neuromorphic System ◽  
Device Properties ◽  
Partial Reset

This work demonstrates the synaptic properties of the alloy-type resistive random-access memory (RRAM). We fabricated the HfAlOx-based RRAM for a synaptic device in a neuromorphic system. The deposition of the HfAlOx film on the silicon substrate was verified by X-ray photoelectron spectroscopy (XPS) analysis. It was found that both abrupt and gradual resistive switching could be implemented, depending on the reset stop voltage. In the reset process, the current gradually decreased at weak voltage, and at strong voltage, it tended to decrease rapidly by Joule heating. The type of switching determined by the first reset process was subsequently demonstrated to be stable switching by successive set and reset processes. A gradual switching type has a much smaller on/off window than abrupt switching. In addition, retention maintained stability up to 2000 s in both switching cases. Next, the multiple current states were tested in the gradual switching case by identical pulses. Finally, we demonstrated the potentiation and depression of the Cu/HfAlOx/Si device as a synapse in an artificial neural network and confirmed that gradual resistive switching was suitable for artificial synapses, using neuromorphic system simulation.

Effect of Threshold Voltage Window and Variation of Organic Synaptic Transistor for Neuromorphic System

2021 ◽  
Vol 21 (8) ◽  
pp. 4303-4309
Author(s):  
Yeongjin Hwang ◽  
Jeong Hoon Jeon ◽  
Juhyun Lee ◽  
Jonghyuk Yoon ◽  
Felix Sunjoo Kim ◽  
...  
Keyword(s):  
Power Consumption ◽  
Threshold Voltage ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Classification Rate ◽  
Data Set ◽  
Organic Transistor ◽  
Handwritten Digit ◽  
Flow Through ◽  
Neuromorphic System

Synaptic devices, which are considered as one of the most important components of neuromorphic system, require a memory effect to store weight values, a high integrity for compact system, and a wide window to guarantee an accurate programming between each weight level. In this regard, memristive devices such as resistive random access memory (RRAM) and phase change memory (PCM) have been intensely studied; however, these devices have quite high current-level despite their state, which would be an issue if a deep and massive neural network is implemented with these devices since a large amount of current-sum needs to flow through a single electrode line. Organic transistor is one of the potential candidates as synaptic device owing to flexibility and a low current drivability for low power consumption during inference. In this paper, we investigate the performance and power consumption of neuromorphic system composed of organic synaptic transistors conducting a pattern recognition simulation with MNIST handwritten digit data set. It is analyzed according to threshold voltage (VT) window, device variation, and the number of available states. The classification accuracy is not affected by VT window if the device variation is not considered, but the current sum ratio between answer node and the rest 9 nodes varies. In contrast, the accuracy is significantly degraded as increasing the device variation; however, the classification rate is less affected when the number of device states is fewer.

scholarly journals Self-Organizing Neural Networks Based on OxRAM Devices under a Fully Unsupervised Training Scheme

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3482 ◽  
Author(s):  
Marta Pedró ◽  
Javier Martín-Martínez ◽  
Marcos Maestro-Izquierdo ◽  
Rosana Rodríguez ◽  
Montserrat Nafría
Keyword(s):  
Learning Algorithm ◽  
Random Access ◽  
Learning Rule ◽  
Resistive Random Access Memory ◽  
Spike Timing ◽  
Self Organization ◽  
Access Memory ◽  
Training Scheme ◽  
Unsupervised Training ◽  
Neuromorphic System

A fully-unsupervised learning algorithm for reaching self-organization in neuromorphic architectures is provided in this work. We experimentally demonstrate spike-timing dependent plasticity (STDP) in Oxide-based Resistive Random Access Memory (OxRAM) devices, and propose a set of waveforms in order to induce symmetric conductivity changes. An empirical model is used to describe the observed plasticity. A neuromorphic system based on the tested devices is simulated, where the developed learning algorithm is tested, involving STDP as the local learning rule. The design of the system and learning scheme permits to concatenate multiple neuromorphic layers, where autonomous hierarchical computing can be performed.

scholarly journals Metal oxide semiconductor nanomembrane–based soft unnoticeable multifunctional electronics for wearable human-machine interfaces

2019 ◽  
Vol 5 (8) ◽  
pp. eaav9653 ◽  
Author(s):  
Kyoseung Sim ◽  
Zhoulyu Rao ◽  
Zhanan Zou ◽  
Faheem Ershad ◽  
Jianming Lei ◽  
...  
Keyword(s):  
Data Storage ◽  
Response Times ◽  
Field Effect Transistors ◽  
Sol Gel ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Body Motion ◽  
Oxide Semiconductor ◽  
Stretchable Electronics ◽  
Human Machine Interfaces

Wearable human-machine interfaces (HMIs) are an important class of devices that enable human and machine interaction and teaming. Recent advances in electronics, materials, and mechanical designs have offered avenues toward wearable HMI devices. However, existing wearable HMI devices are uncomfortable to use and restrict the human body’s motion, show slow response times, or are challenging to realize with multiple functions. Here, we report sol-gel-on-polymer–processed indium zinc oxide semiconductor nanomembrane–based ultrathin stretchable electronics with advantages of multifunctionality, simple manufacturing, imperceptible wearing, and robust interfacing. Multifunctional wearable HMI devices range from resistive random-access memory for data storage to field-effect transistors for interfacing and switching circuits, to various sensors for health and body motion sensing, and to microheaters for temperature delivery. The HMI devices can be not only seamlessly worn by humans but also implemented as prosthetic skin for robotics, which offer intelligent feedback, resulting in a closed-loop HMI system.

Effect of Conductive Filament Temperature on ZrO2 based Resistive Random Access Memory Devices

2020 ◽  
Vol 12 (2) ◽  
pp. 02008-1-02008-4
Author(s):  
Pramod J. Patil ◽  
◽  
Namita A. Ahir ◽  
Suhas Yadav ◽  
Chetan C. Revadekar ◽  
...  
Keyword(s):  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Memory Devices ◽  
Access Memory ◽  
Conductive Filament ◽  
Filament Temperature

scholarly journals Improved Device Distribution in High-Performance SiNx Resistive Random Access Memory via Arsenic Ion Implantation

Nanomaterials ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1401
Author(s):  
Te Jui Yen ◽  
Albert Chin ◽  
Vladimir Gritsenko
Keyword(s):  
High Performance ◽  
Conduction Mechanism ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Switching Behavior ◽  
Access Memory ◽  
Current Voltage ◽  
Current Voltage Characteristics ◽  
Limited Conduction

Large device variation is a fundamental challenge for resistive random access memory (RRAM) array circuit. Improved device-to-device distributions of set and reset voltages in a SiNx RRAM device is realized via arsenic ion (As+) implantation. Besides, the As+-implanted SiNx RRAM device exhibits much tighter cycle-to-cycle distribution than the nonimplanted device. The As+-implanted SiNx device further exhibits excellent performance, which shows high stability and a large 1.73 × 103 resistance window at 85 °C retention for 104 s, and a large 103 resistance window after 105 cycles of the pulsed endurance test. The current–voltage characteristics of high- and low-resistance states were both analyzed as space-charge-limited conduction mechanism. From the simulated defect distribution in the SiNx layer, a microscopic model was established, and the formation and rupture of defect-conductive paths were proposed for the resistance switching behavior. Therefore, the reason for such high device performance can be attributed to the sufficient defects created by As+ implantation that leads to low forming and operation power.

scholarly journals All-inorganic perovskite quantum dot light-emitting memories

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meng-Cheng Yen ◽  
Chia-Jung Lee ◽  
Kang-Hsiang Liu ◽  
Yi Peng ◽  
Junfu Leng ◽  
...  
Keyword(s):  
Random Access ◽  
Optoelectronic Device ◽  
Resistive Random Access Memory ◽  
Access Memory ◽  
Light Emitting ◽  
Inorganic Perovskite ◽  
Emission Color ◽  
Perovskite Quantum Dots ◽  
In Series ◽  
First Time

AbstractField-induced ionic motions in all-inorganic CsPbBr3 perovskite quantum dots (QDs) strongly dictate not only their electro-optical characteristics but also the ultimate optoelectronic device performance. Here, we show that the functionality of a single Ag/CsPbBr3/ITO device can be actively switched on a sub-millisecond scale from a resistive random-access memory (RRAM) to a light-emitting electrochemical cell (LEC), or vice versa, by simply modulating its bias polarity. We then realize for the first time a fast, all-perovskite light-emitting memory (LEM) operating at 5 kHz by pairing such two identical devices in series, in which one functions as an RRAM to electrically read the encoded data while the other simultaneously as an LEC for a parallel, non-contact optical reading. We further show that the digital status of the LEM can be perceived in real time from its emission color. Our work opens up a completely new horizon for more advanced all-inorganic perovskite optoelectronic technologies.

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