Neuro-inspired computing: From resistive memory to optics
Charles Mackin, Pritish Narayanan, et al.
CLEO/Europe-EQEC 2019
Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform composed of more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system’s functionality and accuracy in ion classification.
Charles Mackin, Pritish Narayanan, et al.
CLEO/Europe-EQEC 2019
Stefano Ambrogio, M. Gallot, et al.
IEDM 2019
S. Ambrogio, Pritish Narayanan, et al.
Nature
Katherine Spoon, Stefano Ambrogio, et al.
IMW 2020