An Approach towards Machine Learning Assisted HEMT Based Quantum Dot Sensor for Glucose Detection

In this letter, we have proposed a novel glucose detection sensor that combines the High Electron Mobility Transistor (HEMT) technology with quantum dot and machine learning. The sensor design features an array of quantum dots placed on the gate of the HEMT, having an AlGaAsP/GaAsP heterojunction. When the glucose molecules are introduced, the quantum dots acquire potential, resulting in a gate voltage that modulates the drain current of the HEMT. The variations in the drain current are then analyzed using ANN based machine learning, which has been pre-trained with a comprehensive sample dataset. This algorithm is capable of accurately determining glucose concentrations based on the measured changes in the drain current. By integrating quantum dots with HEMT technology and machine learning, this sensor offers a great approach for precise and reliable glucose sensing.