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001021228 0247_ $$2doi$$a10.1145/3589737.3605966
001021228 037__ $$aFZJ-2024-00665
001021228 1001_ $$0P:(DE-Juel1)192385$$aYang, Ming-Jay$$b0$$eCorresponding author
001021228 1112_ $$aICONS '23: 2023 International Conference on Neuromorphic Systems$$cSanta Fe NM USA$$d2023-08-01 - 2023-08-03$$wUSA
001021228 245__ $$aState-Space Modeling and Tuning of Memristors for Neuromorphic Computing Applications
001021228 260__ $$bACM New York, NY, USA$$c2023
001021228 300__ $$a1-8
001021228 3367_ $$2ORCID$$aCONFERENCE_PAPER
001021228 3367_ $$033$$2EndNote$$aConference Paper
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001021228 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1710238159_2316
001021228 520__ $$aAnalog memristive devices have the potential to merge computing and memory, support local learning, reach high densities, enable 3D stacking, and low energy consumption for neuromorphic computing applications. Yet, integration is challenged by the variability and complex nonlinear dynamics involved in the tuning of memristors, which is required in computing and memory applications. In this paper, we model the dynamic analog switching of memristive devices with an evolution-measurement state-space model. A physics-based compact model is extended to capture statistical distributions of the variability observed in memristors. Based on metal-oxide memristors and electronic measurement data, we applied Sequential-Monte Carlo (Particle Filter) techniques to infer underlying memristor model parameters. The result is validated by experimental data. Applying the calibrated statistical model, we propose an efficient adaptive pulse programming scheme, and performed a comparative analysis across widely applied write-and-verify techniques. We show improved programming control in the metrics of error, energy, and time in reaching target states.
001021228 536__ $$0G:(DE-HGF)POF4-5233$$a5233 - Memristive Materials and Devices (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001021228 536__ $$0G:(DE-Juel1)BMBF-03ZU1106CB$$aBMBF 03ZU1106CB - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - B (BMBF-03ZU1106CB)$$cBMBF-03ZU1106CB$$x1
001021228 588__ $$aDataset connected to CrossRef Conference
001021228 7001_ $$0P:(DE-Juel1)188145$$aPaul Strachan, John$$b1
001021228 773__ $$a10.1145/3589737.3605966
001021228 909CO $$ooai:juser.fz-juelich.de:1021228$$pVDB
001021228 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)192385$$aForschungszentrum Jülich$$b0$$kFZJ
001021228 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188145$$aForschungszentrum Jülich$$b1$$kFZJ
001021228 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5233$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001021228 9141_ $$y2024
001021228 9201_ $$0I:(DE-Juel1)PGI-14-20210412$$kPGI-14$$lNeuromorphic Compute Nodes$$x0
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001021228 980__ $$aI:(DE-Juel1)PGI-14-20210412
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