Simscape and LTspice models of HP ideal generic memristor based on finite closed form solution for window functions

Dautovic, Stanisa; Samardžić, Nataša; Juhas, Anamarija; Ascoli, Alon; Tetzlaff, Ronald

doi:10.1109/icecs53924.2021.9665488

Cited by 3 publications

(11 citation statements)

References 8 publications

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“…The behavior of memristors in an electric field is quite different, in comparison to those of the classical electronic passive elements-the resistor, inductor, and capacitor. Owing to its specifics, the modeling of memristors is mandatory for their analysis by electronic simulators and applications in electronic schemes and devices [19,26]. [19,24,30] Each mathematical model of a memristor is built on two key equations [26].…”

Section: A Short Depiction Of Memristor Elements and Their Operationmentioning

confidence: 99%

“…Owing to its specifics, the modeling of memristors is mandatory for their analysis by electronic simulators and applications in electronic schemes and devices [19,26]. [19,24,30] Each mathematical model of a memristor is built on two key equations [26]. The first one offers the i-v relation, and the second one connects the time derivative of state variable x and the memristor current i (or the memristor voltage v).…”

Section: A Short Depiction Of Memristor Elements and Their Operationmentioning

confidence: 99%

“…The first material memristor based on titanium dioxide (TiO 2 ) was created in Hewlett-Packard scientific and research labs by Stanley Williams and his collaborators [21,23,24]. Different metal oxides, such as HfO 2 , Ta 2 O 5 , Nb 2 O 5 , and many others substances are utilized for the realization of memristor elements [25,26]. Some valuable properties of memristors are their low energy usage, very good switching and memory properties, high switching speed, nano-sizes, and good compatibility with the present day Complementary MOS (CMOS) integrated chips and circuits [27,28].…”

Section: Introductionmentioning

confidence: 99%

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A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors

Mladenov,

Kirilov

2024

Electronics

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Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are potentially applicable in artificial and spiking neural networks, memory arrays, and many other devices and circuits for artificial intelligence. In this paper, a complete electronic realization of an analog circuit model of the modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in the LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an important step towards the forthcoming design of complex memristor-based neural networks for artificial intelligence, for implementation in very high-density integrated circuits and chips.

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Section: A Short Depiction Of Memristor Elements and Their Operationmentioning

confidence: 99%

Section: A Short Depiction Of Memristor Elements and Their Operationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors

Mladenov,

Kirilov

2024

Electronics

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“…The function f N (x) is shown in Figure 1a for N = 1 ∼ 10. Based on the results from [27], the specific advantage of the proposed novel window function is the possibility to determine exact closed-form analytical solutions, expressing the dependence of the charge q (flux ϕ) on the state variable x, in the case of the used model of memristor:…”

Section: Simulation Of Lif Neuron With Volatile Memristormentioning

confidence: 99%

“…Starting from the circuit-level SPICE model, which incorporates both non-volatile and volatile effects in memristor [23], in this paper we included our new window function [27,28] in the memristor's model. The memristor's model was then used in the SPICE simulation of a simple LIF artificial neuron circuit for all-memristive neural networks.…”

Section: Introductionmentioning

confidence: 99%

Volatile Memristor in Leaky Integrate-and-Fire Neurons: Circuit Simulation and Experimental Study

et al. 2022

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In this paper, circuit implementation of a leaky integrate-and-fire neuron model with a volatile memristor was proposed and simulated in the SPICE simulation environment. We demonstrate that simple leaky integrate-and-fire (LIF) neuron models composed of: volatile memristor, membrane capacitance and neuron resistance can mimic spatial and temporal integration, firing function and signal decay. The existing leaky term originates from the recovery of the initial resistive state in the memristor in the spontaneous reset cycle, which is essential for emulating the forgetting process in all-memristive neural networks (MNNs). Furthermore, a diffusive perovskite memristor was used to validate the model where intrinsic memristors’ capacitance acts as neuron membrane capacitance. Good agreement with experimental and simulation results was observed. Volatility, as an inherent property of specific memristors, eliminates the need for usage of an additional peripheral circuit which will reinitialize device state, thus allowing the development of energy-efficient, large scale complex memristive neural networks. The presented circuit level model of LIF neurons can facilitate the design of MNNs.

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All-memristive Spiking Neural Network Circuit Simulator

Vincan

Zoranovic

Samardžić

et al. 2022

2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)

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Simscape and LTspice models of HP ideal generic memristor based on finite closed form solution for window functions

Cited by 3 publications

References 8 publications

A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors

A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors

Volatile Memristor in Leaky Integrate-and-Fire Neurons: Circuit Simulation and Experimental Study

All-memristive Spiking Neural Network Circuit Simulator

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