This project develops and simulates flexible, inkjet-printed memristors for neuromorphic computing and Echo State Networks (ESNs). Using low-cost inkjet printing with materials like graphene and hexagonal boron nitride, the memristors show the essential pinched hysteresis loop for synaptic computing. MATLAB and Simulink are used for empirical and physical modeling, and the memristors are integrated into a crossbar array for ESN implementation, improving data processing efficiency. This approach offers a scalable, energy-efficient solution for future neuromorphic systems.

The integration of machine learning (ML) into flexible electronics has significantly enhanced the processing and analysis of complex, large-scale data generated by sensors. Our research focuses on combining ML algorithms with inkjet-printed sensors to improve accuracy and overcome limitations associated with minimal fabrication processes. Various ML algorithms have been successfully used to analyze physiological signals collected by flexible sensors, driving advancements in health monitoring and other applications. The next step is to implement ML directly on hardware designed specifically for flexible electronics.

Low-power custom integrated analog and RF circuits enable miniaturized energy-efficient system for portable. wearable and implantable systems with extended battery lifetime. Our research focuses on custom integrated circuit (IC) design, simulation, layout, post-layout simulation, and tape out using standard process technology. Notable IC building blocks are operational transconductance amplifier, instrumentation amplifier, digital gates, current starved ring oscillator, differential LC voltage Controlled Oscillator, Power Oscillator, etc.    

Spectrum-efficient short range wireless telemetry is crucial for next generation Internet-Of-Things (IoT) applications. Our group focuses on energy-efficient analog orthogonal pulse generation and pulse sequence based short range high density wireless telemetry without requiring sophisticated digital processing, a noticeable achievement for power-constrained IoT Edge devices. 

Deployment of machine learning models (ML) on resource-constrained devices, referred to as edge devices. These devices, which include microcontrollers and low-power sensors, can perform inference tasks without needing to connect to cloud services.  This innovation enables real-time processing and decision-making at the edge of the network, enhancing performance while minimizing resource consumption.