Memristive operation mode of floating gate transistors: A two-terminal MemFlash-cell

Ziegler, Martin; Oberländer, M.; Schroeder, Dietmar; Krautschneider, Wolfgang H.; Kohlstedt, H.

doi:10.1063/1.4773300

Cited by 38 publications

(30 citation statements)

References 30 publications

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“…Short circuiting the drain contact with lateral gates provides the memristive operation. [35][36][37] The pre-(Vpr) and postsynaptic (Vpo) voltage pulses are applied to the drain and source contacts and emulate the input signals of pre-and postsynaptic neurons, respectively. A resistance with 1 MΩ is used in series to the channel and the measurements are conducted at 4.2 K in the dark.…”

Section: (A)mentioning

confidence: 99%

Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

Maier

Hartmann

Dias

et al. 2016

Journal of Applied Physics

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We present the realization of four different learning rules with a quantum dot memristor by tuning the shape, the magnitude, the polarity and the timing of voltage pulses. The memristor displays a large maximum to minimum conductance ratio of about 57000 at zero bias voltage. The high and low conductances correspond to different amounts of electrons localized in quantum dots, which can be successively raised or lowered by the timing and shapes of incoming voltage pulses. Modifications of the pulse shapes allow altering the conductance change in dependence on the time difference. Hence, we are able to mimic different learning processes in neural networks with a single device. In addition, the device performance under pulsed excitation is emulated combining the Landauer-Büttiker formalism with a dynamic model for the quantum dot charging, which allows explaining the whole spectrum of learning responses in terms of structural parameters that can be adjusted during fabrication such as gating efficiencies and tunneling rates. The presented memristor may pave the way for future artificial synapses with a stimulus-dependent capability of learning.

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Section: (A)mentioning

confidence: 99%

Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

Maier

Hartmann

Dias

et al. 2016

Journal of Applied Physics

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“…The memory resistance (memristance) of memristors [17,18] can originate from different physical mechanisms such as self-heating, chemical reactions, ionic transfer, spin polarization or phase transitions [19]. In addition, a memristive operation mode was observed for floating gate transistors wired as diode connected transistors [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…The memristive operation is realized by short-circuiting the source with the gate contacts (see Fig. 1(b)) [20,21]. This wiring scheme is known as diode connected transistor and can for example be used as temperature sensor [33].…”

Section: Introductionmentioning

confidence: 99%

Electro-Photo-Sensitive Memristor for Neuromorphic and Arithmetic Computing

et al. 2016

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We present optically and electrically tunable conductance modifications of a site-controlled quantum dot memristor. The conductance of the device is tuned by electron localization on a quantum dot. The control of the conductance with voltage and low power light pulses enables applications in neuromorphic and arithmetic computing. As in neural networks, applying pre-and post-synaptic voltage pulses to the memristor allows to increase (potentiation) or decrease (depression) the conductance by tuning the time difference between the electrical pulses. Exploiting state-dependent thresholds for potentiation and depression, we were able to demonstrate a memory-dependent induction of learning. The discharging of the quantum dot can further be induced by low power light pulses in the nW-range. In combination with the state-dependent threshold voltage for discharging, this enables applications as generic building blocks to perform arithmetic operations in bases ranging from binary to decimal with low power optical excitation. Our findings allow the realization of optoelectronic memristor-based synapses in artificial neural networks with a memory-dependent induction of learning and enhanced functionality by performing arithmetic operations.

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“…Since the pioneering work by Strukov et al in 2008, 3 which described resistance switching in thin TiO 2 layers with oxygen deficient with a memristive model, memristive switching has been identified in various material systems including oxide films, 4-6 silver compounds, 7,8 spin memristive systems, 9 and floating-gate transistors. 10,11 Memristors are characterized by a pinched hysteresis loop with a state and time dependent resistance or conductance which depends on the previous charge flow through the device. [1][2][3] This state-dependent conductance enables memristors to emulate artificially the functionality of synapses, 12,13 the connections between neurons in neural networks.…”

mentioning

confidence: 99%