Abstract:formula M n+1 X n T x (n = 1-3) has been studied all over the world, since the discovery of Ti 3 C 2 T x in 2011. [10][11][12][13] These 2D MXenes exhibit many fascinating properties, such as metallic conductivity, [14] high transparency, [15] water dispersibility, [16] thermal stability, [17] good mechanical properties, [18] and antibacterial activity, [19] which are required for functional electronic and optoelectronic devices. Recently, resistive switching properties required to mimic the dynamics of biolog… Show more
“…These characteristics are analogous to biological synapsis potentiation and depression behaviors. 66 These results reveal that the multilevel conductance state can be easily obtained as long as the current limit and stop voltage are carefully controlled during continuous switching operations. As a result, when operated with correct pulse-assisted stimulation, the memristor has the potential to replicate synaptic function.…”
Section: Resultsmentioning
confidence: 73%
“…In addition, 40 levels of analogue resistance reduction (LRS to HRS) were achieved during the reset process by applying sequential positive voltages from 1.0 to 2.0 V with a 0.01 V increment, as shown in Figure c. These characteristics are analogous to biological synapsis potentiation and depression behaviors . These results reveal that the multilevel conductance state can be easily obtained as long as the current limit and stop voltage are carefully controlled during continuous switching operations.…”
Due to their high data-storage capability, oxidebased memristors with controllable conductance properties have attracted great interest in electronic devices for high integration density and neuromorphic synapses. However, high switching uniformity and controllable conductance of memristors during the conversion from a low (ON-state) to a high resistance state (OFFstate) have become essential for their implementation in neural networks. In this study, we fabricate a Pt/HfO 2 /HfAlO x /TiN memristor incorporating atomic-layer-deposited HfO 2 /HfAlO x high-k dielectric thin films as the active material to achieve excellent resistive switching performance with negligible parameter dispersion, multilevel conductance, and neuromorphic synapses for artificial intelligence (AI) systems. This two-terminal memristor exhibits a forming-free switching behavior with outstanding direct current endurance cycles (10 3 ), a high current ON/OFF ratio of >130, stable retention (10 4 s), and multilevel ON-and OFF-state, respectively. Also, memristor conductance/resistance could be modulated through current limits in the set-switching and stop voltage during the reset process, which is useful to acquire a trustworthy analogue switching conduct to mimic the biological neuromorphic synapses. The diverse features of synapses, such as potentiation, depression, spike-rate-dependent plasticity, paired-pulsed facilitation, and spike-time-dependent plasticity, are successfully mimicked in the Pt/HfO 2 /HfAlO x /TiN memristor. Furthermore, the experimental potentiation and depression data are employed for image processing of 28 × 28 pixels comprising 200 synapses. In the Modified National Institute of Standards and Technology database (MNIST), handwritten numbers can be successfully trained to recognize 6000 input images with a training accuracy of about 80%. This Hf-Al-O alloy-based memristor may enable high-density storage memory and realize controllable resistance/weight alteration as a neuromorphic synapse for AI systems.
“…These characteristics are analogous to biological synapsis potentiation and depression behaviors. 66 These results reveal that the multilevel conductance state can be easily obtained as long as the current limit and stop voltage are carefully controlled during continuous switching operations. As a result, when operated with correct pulse-assisted stimulation, the memristor has the potential to replicate synaptic function.…”
Section: Resultsmentioning
confidence: 73%
“…In addition, 40 levels of analogue resistance reduction (LRS to HRS) were achieved during the reset process by applying sequential positive voltages from 1.0 to 2.0 V with a 0.01 V increment, as shown in Figure c. These characteristics are analogous to biological synapsis potentiation and depression behaviors . These results reveal that the multilevel conductance state can be easily obtained as long as the current limit and stop voltage are carefully controlled during continuous switching operations.…”
Due to their high data-storage capability, oxidebased memristors with controllable conductance properties have attracted great interest in electronic devices for high integration density and neuromorphic synapses. However, high switching uniformity and controllable conductance of memristors during the conversion from a low (ON-state) to a high resistance state (OFFstate) have become essential for their implementation in neural networks. In this study, we fabricate a Pt/HfO 2 /HfAlO x /TiN memristor incorporating atomic-layer-deposited HfO 2 /HfAlO x high-k dielectric thin films as the active material to achieve excellent resistive switching performance with negligible parameter dispersion, multilevel conductance, and neuromorphic synapses for artificial intelligence (AI) systems. This two-terminal memristor exhibits a forming-free switching behavior with outstanding direct current endurance cycles (10 3 ), a high current ON/OFF ratio of >130, stable retention (10 4 s), and multilevel ON-and OFF-state, respectively. Also, memristor conductance/resistance could be modulated through current limits in the set-switching and stop voltage during the reset process, which is useful to acquire a trustworthy analogue switching conduct to mimic the biological neuromorphic synapses. The diverse features of synapses, such as potentiation, depression, spike-rate-dependent plasticity, paired-pulsed facilitation, and spike-time-dependent plasticity, are successfully mimicked in the Pt/HfO 2 /HfAlO x /TiN memristor. Furthermore, the experimental potentiation and depression data are employed for image processing of 28 × 28 pixels comprising 200 synapses. In the Modified National Institute of Standards and Technology database (MNIST), handwritten numbers can be successfully trained to recognize 6000 input images with a training accuracy of about 80%. This Hf-Al-O alloy-based memristor may enable high-density storage memory and realize controllable resistance/weight alteration as a neuromorphic synapse for AI systems.
“…The NL extraction method and LTP/LTD curves investigated under various pulse conditions are shown in Figures S13 and S14 (Supporting Information). [ 35 , 36 , 37 ] Finally, we demonstrate its applicability to hardware neural networks (HW‐NNs) using the “DNN+ NeuroSim” simulator. [ 38 ] The artificial neural network of the simulator was a CNN, and the Canadian Institute for Advanced Research‐10 (CIFAR‐10) image dataset was used for training (50 000 images) and inference (10 000 images) tasks.…”
To address the demands of emerging data-centric computing applications, ferroelectric field-effect transistors (Fe-FETs) are considered the forefront of semiconductor electronics owing to their energy and area efficiency and merged logic-memory functionalities. Herein, the fabrication and application of an Fe-FET, which is integrated with a van der Waals ferroelectrics heterostructure (CuInP 2 S 6 /𝜶-In 2 Se 3 ), is reported. Leveraging enhanced polarization originating from the dipole coupling of CIPS and 𝜶-In 2 Se 3 , the fabricated Fe-FET exhibits a large memory window of 14.5 V at V GS = ±10 V, reaching a memory window to sweep range of ≈72%. Piezoelectric force microscopy measurements confirm the enhanced polarization-induced wider hysteresis loop of the double-stacked ferroelectrics compared to single ferroelectric layers. The Landau-Khalatnikov theory is extended to analyze the ferroelectric characteristics of a ferroelectric heterostructure, providing detailed explanations of the hysteresis behaviors and enhanced memory window formation. The fabricated Fe-FET shows nonvolatile memory characteristics, with a high on/off current ratio of over 10 6 , long retention time (>10 4 s), and stable cyclic endurance (>10 4 cycles). Furthermore, the applicability of the ferroelectrics heterostructure is investigated for artificial synapses and for hardware neural networks through training and inference simulation. These results provide a promising pathway for exploring low-dimensional ferroelectronics.
“…The synaptic devices (refs , , , , − , , , , and − ) are structures that incorporate the functions of electronics and optoelectronics and integrate and inspire with neural cells of humans or animals (Table , ,− Figure a,b, , and Figure a–h ,,,,,, ). The development of artificial cells to replace the broken and damaged ones in the human body is in high demand in healthcare (refs , , − , , , , and ).…”
Section: Representative Applications Of 2d Heterostructuresmentioning
A grand family of
two-dimensional (2D) materials and their heterostructures
have been discovered through the extensive experimental and theoretical
efforts of chemists, material scientists, physicists, and technologists.
These pioneering works contribute to realizing the fundamental platforms
to explore and analyze new physical/chemical properties and technological
phenomena at the micro–nano–pico scales. Engineering
2D van der Waals (vdW) materials and their heterostructures via chemical
and physical methods with a suitable choice of stacking order, thickness,
and interlayer interactions enable exotic carrier dynamics, showing
potential in high-frequency electronics, broadband optoelectronics,
low-power neuromorphic computing, and ubiquitous electronics. This
comprehensive review addresses recent advances in terms of representative
2D materials, the general fabrication methods, and characterization
techniques and the vital role of the physical parameters affecting
the quality of 2D heterostructures. The main emphasis is on 2D heterostructures
and 3D-bulk (3D) hybrid systems exhibiting intrinsic quantum mechanical
responses in the optical, valley, and topological states. Finally,
we discuss the universality of 2D heterostructures with representative
applications and trends for future electronics and optoelectronics
(FEO) under the challenges and opportunities from physical, nanotechnological,
and material synthesis perspectives.
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