An Absorbing Markov Chain Model for Stochastic Memristive Devices
- Resource Type
- Conference
- Authors
- Malik, Adil; Papavassiliou, Christos; Stathopoulos, Spyros
- Source
- 2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST) Modern Circuits and Systems Technologies (MOCAST), 2022 11th International Conference on. :1-4 Jun, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Pulse measurements
Computational modeling
Memristors
Switches
Markov processes
Probabilistic logic
Data models
- Language
In this paper we elaborate and verify a data-driven modelling approach, pertaining to the stochastic trajectory of the memristance upon the application of pulses. Our proposed approach is to model the memristor’s behaviour as a time-homogeneous Markov chain. We introduce a simplified method that estimates the states and the state transition probabilities of the model from device measurements. We show that such a memristor model, generally corresponds to an absorbing Markov chain, the physical implications of which are also discussed. We apply this modelling methodology to real-world Pt/TiO2/Pt memristors and present results that validate its effectiveness in capturing the stochastic features of these devices over various timescales.