Magneto-Ionic Physical Reservoir Computing

*Schematic of magneto-ionics based physical reservoir computing.*

Recent progress in magneto-ionics offers exciting potential to leverage its energy efficiency for implementing physical reservoir computing (PRC). Led by postdoctoral fellow Dhritiman Bhattacharya, Prof. Kai Liu’s group have experimentally demonstrated the classification of temporal data using a perpendicularly magnetized magneto-ionic (MI) heterostructure. Their results are recently published in Nano Letters, in collaboration with Prof. Jayasimha Atulasimha’s group at the Virginial Commonwealth University.

The device was specifically engineered to induce nonlinear ion migration dynamics, which in turn imparted nonlinearity and short-term memory (STM) to the magnetization. These 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. This work paves the way for exploiting the relaxation dynamics of solid-state MI platforms and developing energy-efficient MI reservoir computing devices.

Other Georgetown researchers involved include Dr. Christopher Jensen and Dr. Gong Chen. The work was supported in part by the NSF.

Please see here for news coverage by VCU: https://blogs.vcu.edu/engineering/2025/12/18/pushing-the-edge-of-computing-magneto-ionics-imagines-efficient-processing-for-ai-with-reduced-resource-consumption/