T.D. Dongale

Development of Ag/WO3/ITO Thin Film Memristor Using Spray Pyrolysis Method

The unique nonlinear relationship between charge and magnetic flux along with the pinched hysteresis loop in I-V plane provide memory with resistance combinations of attribute to Memristor which lead to their novel applications in non volatile memory, nonlinear dynamics, analog computations and neuromorphic biological systems etc. The present paper reports development of Ag/WO3/ITO thin film memristor device using spray pyrolysis method. The structural, morphological and electrical properties of the thin film memristor device are further characterized using x-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and semiconductor device analyzer. The memristor is simulated using linear dopent drift model to ascertain the theoretical and experimental conformations. For the simulation purpose, the width of doped region (w) limited to the interval [0, D] is considered as a state variable along with the window function characterized by the equation f (x) = w (1 − w). The reported memristor device exhibits the symmetric pinched hysteresis loop in I-V plane within the low operating voltage (±1 V).

TiO2 based nanostructured memristor for RRAM and neuromorphic applications: a simulation approach

We report simulation of nanostructured memristor device using piecewise linear and nonlinear window functions for RRAM and neuromorphic applications. The linear drift model of memristor has been exploited for the simulation purpose with the linear and non-linear window function as the mathematical and scripting basis. The results evidences that the piecewise linear window function can aptly simulate the memristor characteristics pertaining to RRAM application. However, the nonlinear window function could exhibit the nonlinear phenomenon in simulation only at the lower magnitude of control parameter. This has motivated us to propose a new nonlinear window function for emulating the simulation model of the memristor. Interestingly, the proposed window function is scalable up to f(x)xa0=xa01 and exhibits the nonlinear behavior at higher magnitude of control parameter. Moreover, the simulation results of proposed nonlinear window function are encouraging and reveals the smooth nonlinear change from LRS to HRS and vice versa and therefore useful for the neuromorphic applications.

Effect of surfactants on the data directionality and learning behaviour of Al/TiO2/FTO thin film memristor-based electronic synapse

The present communication deals with the development of the titanium dioxide (TiO2) thin films memristor using simple and cost effective hydrothermal route for neuromorphic application. The developed devices show pinched hysteresis loop in current-voltage (I-V) plane, which is the fingerprint characteristic of a memristor. Furthermore, current in the device continuously increases and decreases similar to synaptic weights of the biological neurons. The rectifying property similar to biological synapse is observed in the device which can be converted into the non-rectifying property by the suitable surfactant. The proper surfactant is responsible for the control of data flow in the memristor-based electronic synapse.

Modelling of nanostructured memristor device characteristics using Artificial Neural Network (ANN)

Abstract The present paper reports modelling of nanostructured memristor device characteristics using Artificial Neural Network (ANN). The memristor is simulated using linear drift model and data generated thereof is applied for learning, testing and validation of ANN architecture. In the present investigation we demonstrate optimum ANN architecture for the said modelling by varying the number of hidden neurons and percentage of testing data. The percentage of validation data is varied in order to accomplish tuning of the experiment. Performance of ANN architecture thus derived has been measured in terms of Mean Squared Error (MSE) and Pearson correlation coefficient (r). The hidden units consist of nonlinear sigmoid activation functions and training algorithm is based on a Levenberg–Marquardt Backpropogation method. The reported ANN architecture reveals best performance at lower numbers of hidden neurons and further lower percentage of testing and validation data. Additionally, optimized ANN structure is selected for modelling of other characteristics of memristor such as, flux-charge relation, time domain memristance and width of doped region. The results support, ANN as the preeminent tool for modelling of nonlinear devices such as memristor and the suite of other emerging nanoelectronics devices.

Mimicking the Synaptic Weights and Human Forgetting Curve Using Hydrothermally Grown Nanostructured CuO Memristor Device

In the present investigation, we have fabricated copper oxide (CuO) thin film memristor by employing a hydrothermal method for neuromorphic application. The X-ray diffraction pattern confirms the films are polycrystalline in nature with the monoclinic crystal structure. The developed devices show analog memory and synaptic property similar to biological neuron. The size dependent synaptic behavior is investigated for as-prepared and annealed CuO memristor. The results suggested that the magnitude of synaptic weights and resistive switching voltages are dependent on the thickness of the active layer. Synaptic weights are improved in the case of the as-prepared device whereas they are inferior for annealed CuO memristor. The rectifying property similar to a biological neuron is observed only for the as-prepared device, which suggested that as-prepared devices have better computational and learning capabilities than annealed CuO memristor. Moreover, the retention loss of the CuO memristor is in good agreement with the forgetting curve of human memory. The results suggested that hydrothermally grown CuO thin film memristor is a potential candidate for the neuromorphic device development.