Electro-optical characterization of IC compatible microcantilevers

The aim of this work is to simulate and optically characterize the piezoelectric performance of complementary metal oxide semiconductor (CMOS) compatible microcantilevers based on aluminium nitride (AlN) and manufactured at room temperature. This study should facilitate the integration of piezoelectric micro-electro-mechanical systems (MEMS) such as microcantilevers, in CMOS technology. Besides compatibility with standard integrated circuit manufacturing procedures, low temperature processing also translates into higher throughput and, as a consequence, lower manufacturing costs. Thus, the use of the piezoelectric properties of AlN manufactured by reactive sputtering at room temperature is an important step towards the integration of this type of devices within future CMOS technology standards. To assess the reliability of our fabrication process, we have manufactured arrays of free-standing microcantilever beams of variable dimension and studied their piezoelectric performance. The characterization of the first out-of-plane modes of AlN-actuated piezoelectric microcantilevers has been carried out using two optical techniques: laser Doppler vibrometry (LDV) and white light interferometry (WLI). In order to actuate the cantilevers, a periodic chirp signal in certain frequency ranges was applied between the device electrodes. The nature of the different vibration modes detected has been studied and compared with that obtained by a finite element model based simulation (COMSOL Multiphysics), showing flexural as well as torsional modes. The correspondence between theoretical and experimental data is reasonably good, probing the viability of this high throughput and CMOS compatible fabrication process. To complete the study, X-ray diffraction as well as d33 piezoelectric coefficient measurements were also carried out.

Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.