Back to Search
Start Over
A biodegradable artificial synapse implemented by foundry-compatible materials
- Source :
- Applied Physics Letters. 117:192105
- Publication Year :
- 2020
- Publisher :
- AIP Publishing, 2020.
-
Abstract
- Neuromorphic computing has attracted increasing attention in medical applications due to its ability to improve diagnosis accuracy and human healthcare monitoring. However, the current remote operation mode has a time delay between in vivo data acquisition and in vitro clinical decision-making. Thus, it is of great importance to build a biodegradable neuromorphic network that can operate in a local physiological environment. A biodegradable synapse is a crucial component of such neuromorphic networks. However, the materials employed currently to develop a biodegradable synapse are incompatible with the foundry process, making it challenging to achieve a high density and large-scale neuromorphic network. Here, we report a biodegradable artificial synapse based on a W/Cu/WO3/SiO2/W structure, which is constructed from materials widely used in advanced semiconductor foundries. The device exhibits resistive switching, and the dominated mechanisms are attributed to Ohm's law and trap-filled space charge limited conduction. By manipulating pulse amplitudes, widths, and intervals, the device conductance can be finely regulated to achieve various synaptic functions, such as long-term potentiation, long term depression, paired-pulse facilitation, and spike-rate-dependent plasticity. Moreover, the learning-forgetting-relearning process, which is an essential and complex synaptic behavior, is emulated in a single device. Pattern learning of a slash symbol is also accomplished by building a 4 × 4 synaptic array. In addition, the systematic solubility testing proves its full biodegradability in biofluids. This work opens a potential pathway toward the integration of large-scale neuromorphic network for bioelectronics.
- Subjects :
- 010302 applied physics
Bioelectronics
Physics and Astronomy (miscellaneous)
Computer science
Process (computing)
02 engineering and technology
021001 nanoscience & nanotechnology
01 natural sciences
Synapse
Remote operation
Data acquisition
Neuromorphic engineering
Component (UML)
0103 physical sciences
Electronic engineering
0210 nano-technology
Pattern learning
Subjects
Details
- ISSN :
- 10773118 and 00036951
- Volume :
- 117
- Database :
- OpenAIRE
- Journal :
- Applied Physics Letters
- Accession number :
- edsair.doi...........b3712fa9ab80e3e1ccfd9c15fbbc9691
- Full Text :
- https://doi.org/10.1063/5.0020522