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

Abstract

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) = 1 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.

Recent progress in energy, environment, and electronic applications of MXene nanomaterials

“…278–280 The SET process occurs when the resistance of the device changes from a high-resistance state (HRS) to a low-resistance state (LRS); on the other hand, the converse is called a RESET process. 281,282 Different kinds of RS mechanism have been suggested for memristor devices. These include but are not limited to charge trapping/de-trapping, valence change, ion migration, ferroelectric tunneling, metal–insulator transition, thermochemical reactions, phase change, etc .…”

Design and Evaluation of Memristor-Based Piccolo-80 Lightweight Encryption Algorithm for Future IoT Applications

“…After simulation of the basic modules in SPICE, the memristor was implemented using VTEAM-based Verilog-A codes. VTEAM is a general and accurate enough model that matches the target technology for practical memristive devices and circuits [12][13][14]. The memristor used has the following metrics: Model = 4, Window type = 0, 𝑅 𝑜𝑛 = 50Ω, dt = 1e-10, 𝑅 𝑜𝑓𝑓 = 10kΩ, 𝐾 𝑜𝑛 = -30 m/s, 𝐾 𝑜𝑓𝑓 = 50 m/s, 𝛼 𝑜𝑛 = 3, 𝛼 𝑜𝑓𝑓 = 1, 𝑉 𝑜𝑛 = -0.2V, 𝑉 𝑜𝑓𝑓 = 0.02V, where 𝑅 𝑜𝑛 , 𝑅 𝑜𝑓𝑓 , 𝐾 𝑜𝑛 , 𝐾 𝑜𝑓𝑓 , and 𝛼 𝑜𝑛 are the fitting parameters and 𝑉 𝑜𝑛 and 𝑉 𝑜𝑓𝑓 are SET and RESET voltage threshold.…”

Effect of write voltage and frequency on the reliability aspects of memristor-based RRAM

“…The inherent memory property of memristor is distinctly observed in the nanoscale and therefore, it is considered as a strong candidate for the next generation memories [3]. Along with its applications in memory, there are many interesting applications explored around memristors such as neuromorphic computations [4], in-memory computing [5], biomedical application [6] and much more.…”