The rise of machine learning models for processing sensor data has led to the development of Edge-AI, which aims to perform these data processing tasks locally, directly at the sensor level. This approach reduces the amount of data transmitted and eases the load on centralized computing centers, providing a solution to decrease the overall energy consumption of systems. In this context, the concept of in-sensor computing has emerged, integrating data acquisition and processing within the sensor itself. By leveraging the physical properties of sensors and alternative computing paradigms, such as reservoir computing and neuromorphic computing, in-sensor computing eliminates the energy-intensive steps of signal conversion and processing.

Applying this concept to MEMS sensors enables the processing of signals such as acceleration, strain, or acoustic signals, with a significant reduction, or even elimination, of traditional electronic components. This has rekindled interest in mechanical computing devices and their integration into MEMS sensors like microphones and accelerometers. Recent research explores innovative MEMS devices integrating recurrent neural networks or reservoir computing, showing promising potential for energy efficiency. However, these advancements are still limited to proof-of-concept demonstrations for simple classification tasks with a very low number of neurons.

Building on our expertise in MEMS-based computing, this doctoral work aims to push these concepts further by developing a MEMS device that integrates a reprogrammable neural network with learning capabilities. The objective is to design an intelligent sensor that combines detection and preprocessing on a single chip, optimized to operate with extremely low energy consumption, in the femtoJoule range per activation. This thesis will focus on the design, fabrication, and validation of this new device, targeting low-frequency signal processing applications in high-temperature environments, paving the way for a new generation of intelligent and autonomous sensors.