Abstract
Memristors show promising features for neuromorphic computing. Here we report a soft memristor based on the liquid-vapor surface of a microbubble. The thickness of the liquid film was modulated by electrostatic and interfacial forces, enabling resistance switches. We found a pinched current hysteresis at scanning periods between 1.6 and 51.2 s, while representing a resistor below 1.6 s and a diode-like behavior above 51.2 s. We approximate the thickening/thinning dynamics of liquid film by pressure-driven flow at the interface and derived the impacts of salt concentration and voltage amplitude on the memory effects. Our work opens a new approach to building nanofluidic memristors by a soft interface, which may be useful for new types of neuromorphic computing in the future.
| Original language | English |
|---|---|
| Pages (from-to) | 10475-10481 |
| Number of pages | 7 |
| Journal | Nano Letters |
| Volume | 24 |
| Issue number | 34 |
| DOIs | |
| State | Published - 28 Aug 2024 |
Keywords
- Hebbian learning
- Liquid computing
- Liquid films
- Memristor
- Microbubble
- Soft matter