Magneto-Ionic Physical Reservoir Computing
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911262648041472 |
|---|---|
| author | Rajib, Md Mahadi Bhattacharya, Dhritiman Jensen, Christopher J. Chen, Gong Chowdhury, Fahim F Sarkar, Shouvik Liu, Kai Atulasimha, Jayasimha |
| author_facet | Rajib, Md Mahadi Bhattacharya, Dhritiman Jensen, Christopher J. Chen, Gong Chowdhury, Fahim F Sarkar, Shouvik Liu, Kai Atulasimha, Jayasimha |
| contents | Recent progresses in magnetoionics offer exciting potentials to leverage its non-linearity, short-term memory, and energy-efficiency to uniquely advance the field of physical reservoir computing. In this work, we experimentally demonstrate the classification of temporal data using a magneto-ionic (MI) heterostructure. The device was specifically engineered to induce non-linear ion migration dynamics, which in turn imparted non-linearity and short-term memory (STM) to the magnetization. These capabilities, key features for enabling reservoir computing, were investigated, and the role of the ion migration mechanism, along with its history-dependent influence on STM, was explained. These attributes were utilized to distinguish between sine and square waveforms within a randomly distributed set of pulses. Additionally, two important performance metrics, short-term memory and parity check capacity (PC), were quantified, yielding promising values of 1.44 and 2, respectively, comparable to those of other state-of-the-art reservoirs. Our work paves the way for exploiting the relaxation dynamics of solid-state magneto-ionic platforms and developing energy-efficient magneto-ionic reservoir computing devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Magneto-Ionic Physical Reservoir Computing Rajib, Md Mahadi Bhattacharya, Dhritiman Jensen, Christopher J. Chen, Gong Chowdhury, Fahim F Sarkar, Shouvik Liu, Kai Atulasimha, Jayasimha Mesoscale and Nanoscale Physics Recent progresses in magnetoionics offer exciting potentials to leverage its non-linearity, short-term memory, and energy-efficiency to uniquely advance the field of physical reservoir computing. In this work, we experimentally demonstrate the classification of temporal data using a magneto-ionic (MI) heterostructure. The device was specifically engineered to induce non-linear ion migration dynamics, which in turn imparted non-linearity and short-term memory (STM) to the magnetization. These capabilities, key features for enabling reservoir computing, were investigated, and the role of the ion migration mechanism, along with its history-dependent influence on STM, was explained. These attributes were utilized to distinguish between sine and square waveforms within a randomly distributed set of pulses. Additionally, two important performance metrics, short-term memory and parity check capacity (PC), were quantified, yielding promising values of 1.44 and 2, respectively, comparable to those of other state-of-the-art reservoirs. Our work paves the way for exploiting the relaxation dynamics of solid-state magneto-ionic platforms and developing energy-efficient magneto-ionic reservoir computing devices. |
| title | Magneto-Ionic Physical Reservoir Computing |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2412.06964 |