Here, various synaptic functions and neural network simulation based pattern-recognition using novel, solution-processed organic memtransistors (memTs) with an unconventional redox-gating mechanism are demonstrated. Our synaptic memT device using conjugated polymer thin-film and redox-active solid electrolyte as the gate dielectric can be routinely operated at gate voltages (VGS) below − 1.5 V, subthreshold-swings (S) smaller than 120 mV/dec, and ON/OFF current ratio larger than 108. Large hysteresis in transfer curves depicts the signature of non-volatile resistive switching (RS) property with ON/OFF ratio as high as 105. In addition, our memT device also shows many synaptic functions, including the availability of many conducting-states (> 500) that are used for efficient pattern recognition using the simplest neural network simulation model with training and test accuracy higher than 90%. Overall, the presented approach opens a new and promising way to fabricate high-performance artificial synapses and their arrays for the implementation of hardware-oriented neural network.
Memristive devices are among the most emerging electronic elements to realize artificial synapses for neuromorphic computing (NC) applications and have potential to replace the traditional von-Neumann computing architecture in recent times. In this work, pulsed laser deposition-manufactured Ag/TiO2/Pt memristor devices exhibiting digital and analog switching behavior are considered for NC. The TiO2 memristor shows excellent performance of digital resistive switching with a memory window of order ∼103. Furthermore, the analog resistive switching offers multiple conductance levels supporting the development of the bioinspired synapse. A possible mechanism for digital and analog switching behavior in our device is proposed. Remarkably, essential synaptic functions such as pair-pulse facilitation, long-term potentiation (LTP), and long-term depression (LTD) are successfully realized based on the change in conductance through analog memory characteristics. Based on the LTP-LTD, a neural network simulation for the pattern recognition task using the MNIST data set is investigated, which shows a high recognition accuracy of 95.98%. Furthermore, more complex synaptic behavior such as spike-time-dependent plasticity and Pavlovian classical conditioning is successfully emulated for associative learning of the biological brain. This work enriches the TiO2-based resistive random-access memory, which provides information about the simultaneous existence of digital and analog behavior, thereby facilitating the further implementation of memristors in low-power NC.
Nanocomposites are gaining high demand for the development of next-generation energy storage devices because of their eco-friendly and cost-effective natures. However, their shortterm energy retainability and marginal stability are regarded as hindrances to overcome. In this work, we demonstrate a highperformance supercapacitor fabricated by biocarbon-based MoS 2 (Bio-C/MoS 2 ) nanoparticles synthesized by a facile hydrothermal approach using date fruits. Here, we report the high specific capacitance for a carbon-based nanocomposite employing the pyrolysis technique of converting agricultural biowaste into a highly affordable energy resource. The biocompatible Bio-C/MoS 2 nanospheres exhibited a high capacitance of 945 F g −1 at a current density of 0.5 A g −1 and an excellent reproducing stability of 92% after 10000 charge/discharge cycles. In addition, the Bio-C/MoS 2 NS showed an exceptional power density of 3800−8000 W kg −1 and an energy density of 74.9−157 Wh kg −1 . The results would pave a new strategy for design of eco-friendly materials toward the high-performance energy storage technology.
This work showcases the physical insights of a core-shell dual-gate (CSDG) nanowire transistor as an artificial synaptic device with short/long-term potentiation and long-term depression (LTD) operation. Short-term potentiation (STP) is a temporary potentiation of a neural network, and it can be transformed into long-term potentiation (LTP) through repetitive stimulus. In this work, floating body effects and charge trapping are utilized to show the transition from STP to LTP while de-trapping the holes from the nitride layer shows the LTD operation. Furthermore, linearity and symmetry in conductance are achieved through optimal device design and biases. In a system-level simulation, with CSDG nanowire transistor a recognition accuracy of up to 92.28% is obtained in the Modified National Institute of Standards and Technology (MNIST) pattern recognition task. Complementary metal-oxide-semiconductor (CMOS) compatibility and high recognition accuracy makes the CSDG nanowire transistor a promising candidate for the implementation of neuromorphic hardware.
The remote control of the electrical conductance through nanosized junctions at room temperature will play an important role in future nano-electromechanical systems and electronic devices. This can be achieved by exploiting the magnetostriction effects of ferromagnetic materials. Here we report on the electrical conductance of magnetic nanocontacts obtained from wires of the giant magnetostrictive compound Tb0.3Dy0.7Fe1.95 as an active element in a mechanically controlled break-junction device. The nanocontacts are reproducibly switched at room temperature between “open” (zero conductance) and “closed” (nonzero conductance) states by variation of a magnetic field applied perpendicularly to the long wire axis. Conductance measurements in a magnetic field oriented parallel to the long wire axis exhibit a different behaviour where the conductance switches between both states only in a limited field range close to the coercive field. Investigating the conductance in the regime of electron tunneling by mechanical or magnetostrictive control of the electrode separation enables an estimation of the magnetostriction. The present results pave the way to utilize the material in devices based on nano-electromechanical systems operating at room temperature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.