Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks
In this paper, we present a framework of modeling memristor noise for circuit simulators using physics-informed neural networks (PINNs). The variability of the memristor that is directly related to the neuromorphic system can be handled with this approach. The memristor noise model is transformed in...
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MDPI AG
2024-10-01
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| author | Kyeongmin Kim Jonghwan Lee |
| author_facet | Kyeongmin Kim Jonghwan Lee |
| author_sort | Kyeongmin Kim |
| collection | DOAJ |
| description | In this paper, we present a framework of modeling memristor noise for circuit simulators using physics-informed neural networks (PINNs). The variability of the memristor that is directly related to the neuromorphic system can be handled with this approach. The memristor noise model is transformed into a Fokker–Planck equation (FPE) from a probabilistic perspective. The translated equations are physically interpreted through the PINN. The weights and biases extracted from the PINN are implemented in Verilog-A through simple operations. The characteristics of the stochastic system under the noise are obtained by integrating the probability density function. This approach allows for the unification of different memristor models and the analysis of the effects of noise. |
| format | Article |
| id | doaj-art-e221e1f59d7b4a62b1858caf4b2f5312 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-e221e1f59d7b4a62b1858caf4b2f53122025-08-20T02:11:11ZengMDPI AGApplied Sciences2076-34172024-10-011420948410.3390/app14209484Stochastic Memristor Modeling Framework Based on Physics-Informed Neural NetworksKyeongmin Kim0Jonghwan Lee1Department of System Semiconductor Engineering, Sangmyung University, Cheonan 31066, Republic of KoreaDepartment of System Semiconductor Engineering, Sangmyung University, Cheonan 31066, Republic of KoreaIn this paper, we present a framework of modeling memristor noise for circuit simulators using physics-informed neural networks (PINNs). The variability of the memristor that is directly related to the neuromorphic system can be handled with this approach. The memristor noise model is transformed into a Fokker–Planck equation (FPE) from a probabilistic perspective. The translated equations are physically interpreted through the PINN. The weights and biases extracted from the PINN are implemented in Verilog-A through simple operations. The characteristics of the stochastic system under the noise are obtained by integrating the probability density function. This approach allows for the unification of different memristor models and the analysis of the effects of noise.https://www.mdpi.com/2076-3417/14/20/9484memristornoisestochasticFokker–Planck equation (FPE)physics-informed neural network (PINN)Verilog-A |
| spellingShingle | Kyeongmin Kim Jonghwan Lee Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks Applied Sciences memristor noise stochastic Fokker–Planck equation (FPE) physics-informed neural network (PINN) Verilog-A |
| title | Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks |
| title_full | Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks |
| title_fullStr | Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks |
| title_full_unstemmed | Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks |
| title_short | Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks |
| title_sort | stochastic memristor modeling framework based on physics informed neural networks |
| topic | memristor noise stochastic Fokker–Planck equation (FPE) physics-informed neural network (PINN) Verilog-A |
| url | https://www.mdpi.com/2076-3417/14/20/9484 |
| work_keys_str_mv | AT kyeongminkim stochasticmemristormodelingframeworkbasedonphysicsinformedneuralnetworks AT jonghwanlee stochasticmemristormodelingframeworkbasedonphysicsinformedneuralnetworks |