Modeling Networks of Probabilistic Memristors in SPICE
Efficient simulation of stochastic memristors and their networks requires novel modeling approaches. Utilizing a master equation to find occupation probabilities of network states is a recent major departure from typical memristor modeling [Chaos, solitons fractals 142, 110385 (2021)]. In the presen...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Spolecnost pro radioelektronicke inzenyrstvi
2021-04-01
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| Series: | Radioengineering |
| Subjects: | |
| Online Access: | https://www.radioeng.cz/fulltexts/2021/21_01_0157_0163.pdf |
| Summary: | Efficient simulation of stochastic memristors and their networks requires novel modeling approaches. Utilizing a master equation to find occupation probabilities of network states is a recent major departure from typical memristor modeling [Chaos, solitons fractals 142, 110385 (2021)]. In the present article we show how to implement such master equations in SPICE – a general purpose circuit simulation program. In the case studies we simulate the dynamics of acdriven probabilistic binary and multi-state memristors, and dc-driven networks of probabilistic binary and multi-state memristors. Our SPICE results are in perfect agreement with known analytical solutions. Examples of LTspice code are included. |
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| ISSN: | 1210-2512 |