Computationally efficient memristor model based on Hann window function. (July 2022)

• Record Type: Journal Article
• Authors: Zafar, Mubeen; Awais, Muhammad Naeem; Shehzad, Muhammad Naeem
• Is Part Of: Microelectronics journal. Volume 125(2022)
• Publication Date: 2022-07
• Subjects: Memristor -- Device modeling -- Hann window function -- HSPICE; Microelectronics -- Periodicals; Microélectronique -- Périodiques; Microelectronics; Electronic journals; Journals - contents and abstracts; Periodicals; 621.3805
• DOI: 10.1016/j.mejo.2022.105476
• Languages: English
• ISSNs: 0959-8324

Abstract

Abstract: Memristor is a fourth fundamental passive electric circuit element. A great deal of effort is devoted to the mathematical modeling of a memristor to figure out its complex and dynamic behavior. This research aims to address the demerits of existing mathematical models of the memristor such as computational inefficiency and inaccuracy. A novel model of the memristor with a Hann window function is developed for a Pt–TiO2 –Pt based memristive devices. The threshold values for voltage, frequency and device length are identified. Unlike the existing models, the proposed model has the capability to work with a frequency between 0.5 to 100 Hz. The threshold for a voltage range varies between 0.02 to 3 V. The memristance was observed only if a device length of the proposed model varies between 5 to 250 nm. It is inferred that a memory window skews with an increase in applied voltage frequency and device length. Whereas memory window increases with the increase in applied voltage. The scaling parameter ( j ) is incorporated in the proposed model to make it more scalable and flexible. Results depict that the proposed model simulation runtime and elapsed time improved by 36% and 33% respectively. The accuracy of the presented model is 1.748 in terms of relative root mean square error. The proposed model exhibits low complexity with sufficient accuracy in simple, intuitive and closed-form.