Dima, Alina Elena (2022) A Physically Inspired Memristor Model of the Ni/Nb:SrTiO3 Schottky Interface and Its Applications. Bachelor's Thesis, Artificial Intelligence.
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Abstract
Since the amount of data that companies and individuals use globally is increasing exponentially, processing performance should increase at the same rate. However, an exponential increase in processing power is no longer achievable due to the physical limitations of traditional computers. Neuromorphic computing represents a promising alternative because it is faster and more energy efficient. One example is brain-inspired learning using memristors, as the resistance of these devices varies based on the voltage history. Hence, they can be a natural fit for simulating synapses, whose weights vary as a function of time. The phenomenon is known as spike timing-dependent plasticity, which we aim to model. This study uses empirical data obtained from the Ni/Nb:SrTiO3 Schottky interface to propose a physically plausible mathematical model. This model is then used to show that spike timing-dependent plasticity can indeed be simulated using the Ni/Nb:SrTiO3 memristive device. Since synapses are at the core of brain-inspired learning and we can artificially simulate them, our study strengthens the belief that neuromorphic computing could one day replace the outdated traditional approach.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Borst, J.P. and Tiotto, T.F. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 15 Jul 2022 14:07 |
| Last Modified: | 15 Jul 2022 14:07 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/27864 |
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