An electroforming-free mechanism for Cu 2 O solid-electrolyte-based conductive-bridge random access memory (CBRAM)

Woo

et al. 2022

Adv Elect Materials

Self Cite

Neumann architectures to overcome the von Neumann bottleneck in artificial intelligence applications. [1][2][3][4][5][6][7][8] Neuromorphic architectures, especially spiking neural networks (SNNs), consume considerably less power (≈20 mW), than conventional von Neumann computing architectures (≈100 W). [9] As the main building block of SNNs, spiking neurons, especially complementary-metal-oxidesemiconductor field-effect transistor (C-MOSFET)-based neurons, have been intensively researched. However, the density of such neurons is limited because of the extremely large area of the capacitor (>500 μm 2 per capacitor) required to emulate the integrate function and achieve sufficient capacitance (i.e., several pF per capacitor). [10][11][12][13][14][15][16][17][18] To overcome this problem, the use of capacitor-less spiking neurons has been recommended. Recently, several researchers reported spiking neurons that can exhibit the integrate functionality, based on frameworks such as the phase-change memory, [19,20] resistive-random-access memory, [21][22][23][24][25] conductive-bridge-random-access memory, [26,27] and partially depleted silicon-on-insulator. [28] A spiking neuron principally needs a spike neuron device to emulate the integrate function and a sensing amplifier circuit to generate the fire function in a scheme known as integrate-and-fire. However, the existing studies only empirically presented spiking neuron devices to emulate the integrate function. Certain researchers designed a neuron in software by using a sense amplifier circuit, a controller for operating the neuron, a SNN containing neurons, and synapses, and a pattern recognition test was conducted using software simulations. However, only the realization of a spiking neuron was demonstrated as all the SNN operations based on spiking neuron devices emulating the integrate function were conducted via simulations.This study represents the first attempt at developing a conductive-bridge neuron emulating an integrate-and-fire function as an alternative to conventional C-MOSFET-based spiking neurons. The neuron was composed of a conductive-bridge-neuron device, sensing amplifier, and latch circuit. The conductivebridge-neuron device was fabricated in a simple manner by adopting a vertical device structure including a CuTe top electrode, TiO 2 resistive layer, and TiN bottom electrode. The Cu atoms diffused from the CuTe top electrode could easily form Cu filaments in the TiO 2 resistive layer. This phenomenon is of significance because Cu filaments in the TiO 2 resistiveThe neuronal density of complementary metal-oxide-semiconductor field-effect transistor-based neurons is limited because of the use of capacitors. Therefore, a novel neuron is fabricated using a conductive-bridge-neuron device, currentmirror-type sense amplifier, latch, micro-controller-unit, and digital-analogconverters. This neuron exhibits a typical integrate-and-fire function; in particular, the generation frequency of the fire spikes at the neuron exponentially increases with the i...

Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Real‐Time Correlation Detection via Online Learning of a Spiking Neural Network with a Conductive‐Bridge Neuron

Woo

et al. 2022

Adv Elect Materials

Self Cite

“…Finally, a precise design of the Cu ion concentration profile in an α‐chalcogenide alloy is crucial for achieving selector cell or conductive‐bridge‐random‐access‐memory (CBRAM) cell behavior and the presence or absence of a forming process. [ 22–28 ]…”

Section: Resultsmentioning

confidence: 99%

Super‐Linear‐Threshold‐Switching Selector with Multiple Jar‐Shaped Cu‐Filaments in the Amorphous Ge₃Se₇ Resistive Switching Layer in a Cross‐Point Synaptic Memristor Array

et al. 2022

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The learning and inference efficiencies of an artificial neural network represented by a cross‐point synaptic memristor array can be achieved using a selector, with high selectivity (Ion/Ioff) and sufficient death region, stacked vertically on a synaptic memristor. This can prevent a sneak current in the memristor array. A selector with multiple jar‐shaped conductive Cu filaments in the resistive switching layer is precisely fabricated by designing the Cu ion concentration depth profile of the CuGeSe layer as a filament source, TiN diffusion barrier layer, and Ge3Se7 switching layer. The selector performs super‐linear‐threshold‐switching with a selectivity of > 107, death region of −0.70–0.65 V, holding time of 300 ns, switching speed of 25 ns, and endurance cycle of > 106. In addition, the mechanism of switching is proven by the formation of conductive Cu filaments between the CuGeSe and Ge3Se7 layers under a positive bias on the top Pt electrode and an automatic rupture of the filaments after the holding time. Particularly, a spiking deep neural network using the designed one‐selector‐one‐memory cross‐point array improves the Modified National Institute of Standards and Technology classification accuracy by ≈3.8% by eliminating the sneak current in the cross‐point array during the inference process.

“…A bi‐stable resistance of CBRAM was obtained by electroforming or a rupture of metal ion filaments in the resistive layer. [ 41–44 ]…”

Section: Resultsmentioning

confidence: 99%

Bi‐Stable Resistance Generation Mechanism for Oxygenated Amorphous Carbon‐Based Resistive Random‐Access Memory

Jin

Woo

et al. 2022

Adv Elect Materials

Self Cite

The oxygenated amorphous carbon (α‐COx)‐based resistive random‐access memory (ReRAM) generates a bi‐stable resistance via electroforming or the rupture of the conductive CC sp2 covalent bond filaments in the α‐COx resistive layer, which can be determined by the dependency of the intensity distribution of the oxygen ion (O2−), for the 3D cross‐point nonvolatile memory as the new memory hierarchical structure for an artificial neural network. The conductive CC sp2 and insulating CC sp3 covalent bonds are formed near the top of the resistive layer by drifting and diffusing O2− toward the bottom and top parts of the layer in the set and reset processes, respectively. The reset process has a different bi‐stable resistance generation mechanism from binary metal oxide‐based ReRAM and conductive bridge random‐access memory. The conductive CC sp2 covalent bond intensity in the α‐COx resistive layer affects the forming voltage and the write and erase endurance cycle of the α‐COx‐based ReRAM cells. The result shows that a lower proportion of conductive CC sp2 covalent bond leads to longer write and erase endurance cycles.