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Mechanisms for Enhanced State Retention and Stability in Redox-Gated Organic Neuromorphic Devices
摘要： Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. One promising contender for efficient ANN implementation is crossbar arrays of resistive memory elements that emulate the synaptic strength between neurons within the ANN. Organic nonvolatile redox memory has recently been demonstrated as a promising device for neuromorphic computing, offering a continuous range of linearly programmable resistance states and tunable electronic and electrochemical properties, opening a path toward massively parallel and energy efficient ANN implementation. However, one of the key issues with implementations relying on electrochemical gating of organic materials is the state-retention time and device stability. Here, revealed are the mechanisms leading to state loss and cycling instability in redox-gated neuromorphic devices: parasitic redox reactions and out-diffusion of reducing additives. The results of this study are used to design an encapsulation structure which shows an order of magnitude improvement in state retention and cycling stability for poly(3,4-ethylenedioxythio phene)/polyethyleneimine:poly(styrene sulfonate) devices by tuning the concentration of additives, implementing a solid-state electrolyte, and encapsulating devices in an inert environment. Finally, a comparison is made between programming range and state retention to optimize device operation.
关键词： resistive memory,PEDOT:PSS,polymer semiconductor,artificial synapse,neural network
更新于2025-09-23 15:21:01
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[IEEE 2019 4th Scientific International Conference Najaf (SICN) - Al-Najef, Iraq (2019.4.29-2019.4.30)] 2019 4th Scientific International Conference Najaf (SICN) - Simulation of Solar Cell and sinusoidal pulse width modulation Inverter Using MATLAB and Proteus
摘要： Resistive switching memory (RRAM) has been proposed as an artificial synapse in neuromorphic circuits due to its tunable resistance, low power operation, and scalability. For the development of high-density neuromorphic circuits, it is essential to validate the state-of-the-art bistable RRAM and to introduce small-area building blocks serving as artificial synapses. This paper introduces a new synaptic circuit consisting of a one-transistor/one-resistor structure, where the resistive element is a HfO2 RRAM with bipolar switching. The spike-timing-dependent plasticity is demonstrated in both the deterministic and stochastic regimes of the RRAM. Finally, a fully connected neuromorphic network is simulated showing online unsupervised pattern learning and recognition for various voltages of the POST spike. The results support bistable RRAM for high-performance artificial synapses in neuromorphic circuits.
关键词： memristive device,neuromorphic network,pattern learning,Artificial synapse,resistive switching memory (RRAM)
更新于2025-09-19 17:13:59
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Dual Modes Electronic Synapse Based on Layered SnSe Films Fabricated by Pulsed Laser Deposition
摘要： Artificial synapse, such as memristive electronic synapse, caught world-wide attention, attributed to its potential in neuromorphic computing which may tremendously reduce the computer volume and energy consumption. Introducing of layered two-dimentional materials has been reported enhance performance of the memristive electronic synapse. However, it is still a challenge to fabricate large-area layered two-dimentioanl films by scalable methods, which has greatly limit the industrial application potential of two-dimentioanl materials. In this work, a scalable pulsed laser deposition (PLD) method has been ultilized to fabricate large-area layered SnSe films, which is used as the functional layer of memristive electronic synapse with dual modes. Both long-term memristive behaviour with gradually changed resistance (Mode 1) and short-term memristive behavior with abruptly reduced resistance (Mode 2) have been ahieved in this SnSe based memristive electronic synapse . The switching between Mode 1 and Mode 2 can be realized by series of voltage sweeping and programed pulses. Formation and recovery of Sn vacancies were believed to induce the short-term memristive behaviour, and the joint action of Ag filament formation/rupture and Schottky barrier modulation should be the origin of long-term memristive behaviour. DFT calculation was performed to further illustrate how Ag atoms and Sn vacancies diffuse through SnSe layer and form filaments. The successful emulation of synaptic functions by layered chalcogenides memristor fabricated by PLD method suggestes the application potential in future neuromorphic computers.
关键词： neuromorphic computing,layered two-dimentional materials,pulsed laser deposition,SnSe films,memristive electronic synapse,Artificial synapse,dual modes
更新于2025-09-16 10:30:52
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[IEEE 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT) - Qingdao, China (2018.10.31-2018.11.3)] 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT) - Non-filamentary Pd/Al<inf>2</inf>O<inf>3</inf>/TaO<inf>x</inf>/Ta Memristor as Artificial Synapse for Neuromorphic Computing
摘要： We report a fully CMOS compatible bilayer, forming-free and non-filamentary memristive device with excellent bidirectional analog switching behavior as artificial synapse for neuromorphic computing applications. The bilayer stack structure, consisting of 8 nm TaOx formed via oxidation process on Ta bottom electrode and 7 nm Al2O3 via atom layer deposition (ALD), is sandwiched between the Ta bottom electrode and Pd top electrode. The Pd/Al2O3/TaOx/Ta device shows bidirectional analog resistive switching behaviors, and multilevel conductance states (>60) with satisfying retention time can be obtained. Long term plasticity, consisting of long-term potentiation (LTP) and long-term depression (LTD), have been demonstrated based our device. And a nearly linear conductance change behavior is obtained by optimizing the training scheme: adopting non-identical training pulses. A two-layer perceptron neural network was performed to estimate the synapse characteristics of our devices. More than 94% recognition accuracy of MNIST handwritten digit dataset are achieved. Based on these results, the device is a promising emulator for biology synapse, and has a great potential to be used in neuromorphic systems.
关键词： memristor,neuromorphic computing,analog switching,artificial synapse,CMOS compatible
更新于2025-09-09 09:28:46