Dual SOT Switching Modes in a Single Device Geometry for Neuromorphic Computing
Neuromorphic computing aims to replicate the brain's efficient processing through artificial neurons and synapses, requiring binary and multilevel switching. We present a PtMn/(Co/Pd)<sub>4</sub>/Ta device that uniquely enables dual spin-orbit torque (SOT) switching modes─binary and multilevel (analog)─within the same geometry and stack structure, eliminating the need for device modifications. Binary SOT switching is achieved via domain wall nucleation and propagation at moderate current levels (∼65 mA), while multilevel switching occurs via domain nucleation mode without significant propagation after a high-current treatment (∼85 mA). The transition between two modes originates from structural changes after the current treatment. These modes allow for neuronal and synaptic functionalities, with the device achieving 96% accuracy in digit/letter recognition on the MNIST data set using an artificial neural network (ANN). The device's robust perpendicular magnetic anisotropy (PMA), dual-mode switching under a small in-plane field (<i>H</i><sub>X</sub>), and simplified fabrication underscore its promise as an energy-efficient solution for neuromorphic computing.
Authors
- Tamkeen Farooq (ORCID: https://orcid.org/0000-0002-4387-8097)
- Hsin‐Ya Sung
- Rudis Ismael Salinas Padilla (ORCID: https://orcid.org/0000-0002-0758-4489)
- Wen‐Wei Wu (ORCID: https://orcid.org/0000-0002-8388-8417)
- Abhijeet Ranjan (ORCID: https://orcid.org/0000-0002-8127-5799)
- Ming‐Yen Lu (ORCID: https://orcid.org/0000-0003-1788-1425)
- Chong-Chi Chi
- Rahul Mishra (ORCID: https://orcid.org/0000-0001-5336-4260)
- Chih‐Huang Lai (ORCID: https://orcid.org/0000-0002-9250-0015)
- Po-Hung Lin (ORCID: https://orcid.org/0000-0002-7329-5140)
Institutions
- National Yang Ming Chiao Tung University (TW)
- National Tsing Hua University (TW)
- Indian Institute of Technology Delhi (IN)
Publication Details
- Journal
- Nano Letters
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1021/acs.nanolett.5c01100
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46
Funders
- National Science and Technology Council