Suspended microchannel resonators are a kind of mechanical resonator with an embedded microchannel inside. This is done as for some samples it is convenient or even necessary to put the sample into a fluid. The sample can then travel through the channel and its mass can be determined. The channel also simplifies placement of the samples compared to regular nanomechanical resonators. By having this channel embedded within the resonator, it is then possible to place the resonator as a whole in a vacuum and so eliminate medium losses. The main purpose of these suspended microchannel resonators is mass spectrometry which has already been performed on viruses and biological cells and so could potentially be very useful for the medical field as well. Especially if suspended microchannel resonators can be used to measure individual proteins which has not been done before with these resonators. It would also be interesting to use these kinds of resonators in quantum mechanical experiments. This means that the performance of these suspended microchannel resonators needs to be improved.

In order to accomplish this, a new kind of suspended microchannel resonator is introduced. The determination of these designs is strictly FEM based. The new resonator design, which is a doubly clamped beam, is made from pre-stressed silicon nitride which enables high Q-factors caused by an effect known as dissipation dilution. Dissipation dilution means diluting the energy losses of the system by increasing the stored energy which is increased by the initial stress in the silicon nitride. To increase this effect, the resonator is tapered towards the center. This is called strain engineering. Silicon nitride has been used in suspended microchannel resonators before but it did not result in higher Q-factors. Possibly due to clamping losses. The designs presented here offer a solution to that in the form of soft-clamping. This reduces the curvature near the boundaries to a minimum which means that the clamping losses are also reduced or eliminated. The combined effect of these features results in very high Q-factors as has already been reported for nanomechanical resonators. Through machine learning optimization it is possible to achieve 6E12 Hz in Qf product required for quantum mechanical experiments at room temperature and more than one order of magnitude improvement in mass sensitivity.