On collective behaviour of coupled micro/nano electromechanical sensors

With advances in nanotechnology, resonant sensors based on micro/nano electromechanical systems (M/NEMS) have been manufactured utilising a process similar to that of microelectronics. By providing a frequency shift proportional to the mass/stiffness change, these small resonators enable rapid detection of extremely minute changes in mass and force. For example, they can be used to trace the concentration of pathogens. M/NEMS resonators also demonstrate excellent compatibility with electronic circuits, hence offering multi-function and single-chip solutions for next-generation sensing applications. Resonators are coupled to provide extra degrees of freedom for multi-sensing, while reducing a majority of connections required. The collective behaviour of coupled systems gives rise to new sensing methods such as eigenvalue and eigenvector sensing, which exhibit enhanced sensitivity and linearity as opposed to the traditional frequency-shift approach. However, the mechanisms of coupling have not yet been fully exploited. Coupled micro/nano systems are exposed to several challenges. First, process variability is known to degrade sensor performance. Moreover, the readout and signal processing issues in large arrays have not been addressed. Hence, actuating and testing coupled sensors are expensive and time-consuming. These hurdles can be circumvented using a novel inverse eigenvalue analysis (IEA) method proposed in this thesis, which is capable of characterising coupled systems based on inverse and forward analysis. The method extracts the system matrix, which carries deterministic information about process and sensitivity, by attaching a peripheral electrical resonator to the MEMS array, therefore providing the advantages of simplified actuation, accurate calibration and sensitivity trimming. To explore the ultimate limit of scaling, prototype sensors consisting of different numbers of coupled resonators have been designed, fabricated and tested for sensing. By investigating the unique behaviour of eigenvalues, various approaches have been proposed to enhance the accuracy of inverse eigenvalue sensing in large coupled systems.