Abstract
With the recent advances in memristors as a potential building block for future hardware, it becomes an important and timely topic to study on memristor modelling. Memristor models are important for designers to exhibit memristor behavior since memristor is not yet available in market. An ideal memristor behavior has been remodel with Generalized Regression Neural Network (GRNN) and presented in this paper. Mathematical equations are used with a set of given memristor process parameters such as R ON , R OFF , thickness of TiO 2 , and instantaneous memristor behaviour is modelled. The behavior of this model is in agreement with the calculations of HP Lab's and Joglekar's SPICE model.
Keywords
Memristor
Artificial neural network
Set (abstract data type)
Computer science
Artificial intelligence
Block (permutation group theory)
Spice
Algorithm
Machine learning
Mathematics
Electronic engineering
Engineering
Programming language
Combinatorics
Subject Areas
Advanced Memory and Neural Computing
·Electrical and Electronic Engineering ·Physical Sciences
Ferroelectric and Negative Capacitance Devices
·Electrical and Electronic Engineering ·Physical Sciences
Transition Metal Oxide Nanomaterials
·Polymers and Plastics ·Physical Sciences
Citations by Year
2018: 1
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