Self-Powered Memory Systems

Ranjan

ACS Appl. Electron. Mater.

et al. 2021

Self Cite

The memristor-based neuromorphic computing application, which is highly flexible and capable of handling large amounts of parallel information, is one of the major breakthroughs in the past decade. It sheds light on future high-density storage, ultrafast logic computing systems, advanced artificial intelligence, etc. To explore the applications based on memristive devices, a memristive device using an indium−gallium−zinc oxide (IGZO)− TiO 2 bilayer film as the functional layer is demonstrated. Its memristive behavior can be modulated by diethylmethyl(2methoxyethyl)ammonium bis(trifluoromethylsulfonyl)imide (DEME-TFSI) ionic liquid (IL). Further, a true random number generator was designed on the basis of eight memristive units. This work demonstrates that ionic liquid regulated memristors can not only be used for data storage but also open up potential applications for cryptography.

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A True Random Number Generator Based on Ionic Liquid Modulated Memristors

Ranjan

ACS Appl. Electron. Mater.

et al. 2021

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“…The V o -and H 2 O-related redox reaction is easily triggered. [196][197][198][199][200][201] The V o possibly is compensated or weaken by the electron contributed by the redox reaction or light stimulation. In this case, the unique physical effects such as the NDR and are possibly emerging in the anion-based memristor.…”

Section: General Memristormentioning

confidence: 99%

Volatile and Nonvolatile Memristive Devices for Neuromorphic Computing

Wang

et al. 2022

Adv Elect Materials

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140

(5 of 33)www.advelectronicmat.de state after operation an external stimulation (Figure 2f). As the increasing stimulation, the transition state entering to the metallic state leads to the Mott layer with low resistance state (LRS) (Figure 2g). The insulator to metal transition is in a timescale of femtosecond and picosecond, [69] while from the opposite transition from the metal state to the insulator state Figure 3. Second-order memristor for temporal information simulation. a) Conception of the second-order memristor. Adapted with permission. [51] Copyright 2015, American Chemical Society. b) Schematic of an artificial neuron consisting of dendrites, soma, and axon constructed by the secondorder memristor circuit. c) The temporal summation of excitatory postsynaptic currents (EPSCs) for the frequency-dependency conductance evolution of 2nd memristor. Adapted with permission. [52] Copyright 2018, Wiley. d) The second-order memristor networks consist of 128 inputs and 7 outputs for temporal learning simulation, from up to bottom denotes before training state with random weights and different learned motion speeds. Adapted with permission. [49] Copyright 2017, IEEE. e) Transient temperature evolution with Δt = 1 µs and Δt = 100 ns. Adapted with permission.

“…The increase and decrease of conductance can simulate the potentiation and depression of synapses . The ReRAM has the advantages of nonvolatile, fast read/write speeds, ultrasmall size, and low power consumption . The resistance states can be changed by regulating the flux of charge carriers (ie, ions and electron carriers) flowing through the device. , Many physical models have been reported to explain the memristive behaviors of ReRAM devices.…”

Section: Introductionmentioning

confidence: 99%

A Battery-Like Self-Selecting Biomemristor from Earth-Abundant Natural Biomaterials

Guo

et al. 2021

ACS Appl. Bio Mater.

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Using the earth-abundant natural biomaterials to manufacture functional electronic devices meets the sustainable requirement of green electronics, especially for the practical application of memristors in data storage and neuromorphic computing. However, the sneak currents flowing though the unselected cells in a large-scale cross-bar memristor array is one of the major problems which need to be tackled. The self-selecting memristors can solve the problem to develop compact and concise integrated circuits. Here, a sustainable natural biomaterial (anthocyanin, C15H11O6) extracted from plant tissue is demonstrated for ions and electron transport. The capacitive-coupled memristive behavior of as-prepared bioelectronic device can be significantly modulated by diethylmethyl(2-methoxyethyl)ammoium bis(trifluoromethylsulfonyl)imide (DEME-TFSI) ionic liquid (IL). Furthermore, graphene was inserted into biomaterial matrix to manipulate the memristive effects by graphene protonation. This results in a battery-like self-selective memristive effect. This phenomenon is explained by a physical model and density functional theory (DFT) based first-principles calculations. Finally, the self-selective behavior was applied in 0T-1R array configuration, which indicates the battery-like self-selecting biomemristor has potential applications in the brain-inspired computing, data storage systems, and high-density device integration.