Learning processes modulated by the interface effects in a Ti/conducting polymer/Ti resistive switching cell

Zeng, Fei; Li, Sizhao; Yang, Jing; Pan, Feng; Guo, Dong

doi:10.1039/c3ra46679e

Cited by 59 publications

(43 citation statements)

References 32 publications

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“…Further analysis, unlike metal conductive filaments or ion migration mechanisms, the device conductance with a partial free rise under the action of an electric field and a uniform smooth decay over time is often considered to be related to charge trapping/release . The trapping and release of charge on the interface causes a change in the interface barrier, which in turn leads to resistance switching behavior.…”

Section: Resultsmentioning

confidence: 99%

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Zhu

et al. 2020

Adv Funct Materials

161

129

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Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.

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Section: Resultsmentioning

confidence: 99%

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Zhu

et al. 2020

Adv Funct Materials

161

129

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“…As shown in Figure a, the memristor behavior is related to the movement of the Ag interface, which is induced by a redox reaction under electric bias; hence, this configuration may underlie the observed rectification, high resistance and low power consumption (Figure b). This verdict was verified in their other study by changing the electrode to Ti . A series of synaptic behaviors, such as STP, LTP, STDP and SRDP were successfully emulated.…”

Section: Active Materials and Dopantsmentioning

confidence: 55%

Nanoionics‐Enabled Memristive Devices: Strategies and Materials for Neuromorphic Applications

Wang

Nagai

et al. 2017

Adv Elect Materials

182

124

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Memristors have been intensively studied in recent years as potential building blocks for the construction of versatile neuromorphic architectures. The prevalent developments focus on nanoionic devices due to their ease of reversible regulation, non-volatility, scalability down to the atomic level, low-energy consumption, nanosecond response times, sufficient yield and reliability, as well as their homologous regulatory mechanisms with biological synapses. However, despite their desirable structures and excellent properties, the previously reported properties of nanoionic devices are rather diverse and dissatisfactory for the delivery of reliable analog properties, which is the precondition for the imitation of brain-like functions. Here, the general requirements of neuromorphic engineering in terms of device structure, characterization parameters, synaptic functions and device engineering are introduced. A critical overview of the proposed nanoionic mechanisms for memristive switching is given, focusing particularly on providing fundamental insights into the strategies for regulating the adaptive memristive characteristics of devices that resemble the behaviors of biological synapses, which is an element of neural networks. In addition, the research progress in active materials (e.g., oxides, polymers, small molecules, bio-macromolecules and dopants), especially regarding the correlation among materials, is elaborated and a rational approach to provide insight into new design strategies is considered, and how to understand the memristor mechanisms and further achieve excellent memristive devices is discussed. Finally, the current challenges and several possible future research directions in this area are discussed.

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“…WORM memory mechanism in PEDOT: PSS or related composites has been a well-known effect1920 while their memristor phenomena are still in debate or discussion212223. To gain better insight on the governing mechanisms of the memristor mode, one needs to exclude spurious effect from metallic filaments mechanism from electromigration of top or bottom electrodes.…”

Section: Resultsmentioning

confidence: 99%

“…The films were annealed at 120 °C in Ar filled glovebox for 2 h. The top gold electrodes were evaporated through shadow mask using thermal evaporator at 1 × 10 −4 Torr and deposition rate at 0.005 nm/s using in- situ crystal quartz. Using gold electrodes (in 0.2 mm diameter), we avoid the complicated oxide interface between PEDOT: PSS and reactive metal electrodes such as Titanium or Aluminum2223. Such oxide interface has been pointed out many times as the main factor for resistive switching in organic materials.…”

Section: Methodsmentioning

confidence: 99%

Coexistence of Write Once Read Many Memory and Memristor in blend of Poly(3,4-ethylenedioxythiophene): polystyrene sulfonate and Polyvinyl Alcohol

Nguyen

Lee

2016

Sci Rep

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In this work, the coexistence of Write Once Read Many Memory (WORM) and memristor can be achieved in a single device of Poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT: PSS) and Polyvinyl Alcohol (PVA) blend organic memory system. In memristor mode, the bistable resistance states of the device can be cycled for more than 1000 times. Once a large negative bias of −8V was applied to the device, it was switched to permanent high resistance state that cannot be restored back to lower resistance states. The mechanism of the memristor effect can be attributed to the charge trapping behaviour in PVA while the WORM effect can be explained as the electrochemical characteristic of PEDOT: PSS which harnesses the percolative conduction pathways. The results may facilitate multipurpose memory device with active tunability.

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Learning processes modulated by the interface effects in a Ti/conducting polymer/Ti resistive switching cell

Cited by 59 publications

References 32 publications

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Optoelectronic Perovskite Synapses for Neuromorphic Computing

Nanoionics‐Enabled Memristive Devices: Strategies and Materials for Neuromorphic Applications

Coexistence of Write Once Read Many Memory and Memristor in blend of Poly(3,4-ethylenedioxythiophene): polystyrene sulfonate and Polyvinyl Alcohol

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