A novel taxonomy of built-in self-test (BIST) methods is presented for the testing of micro-electro-mechanical systems (MEMS). With MEMS testing representing 50% of the total costs of the end product, BIST solutions that are cost-effective, non-intrusive and able to operate non-intrusively during system operation are being actively sought after. After an extensive review of the various testing methods, a classification table is provided that benchmarks such methods according to four performance metrics: ease of implementation, usefulness, test duration and power consumption. The performance table provides also the domain of application of the method that includes field test, power-on test or assembly phase test. Although BIST methods are application dependent, the use of the inherent multi-modal sensing capability of most sensors offers interesting prospects for effective BIST, as well as built-in self-repair (BISR).

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Separation in microfluidic devices is a crucial enabling step for many industrial, biomedical, clinical or chemical applications. Acoustic methods offer contactless, biocompatible, scalable sorting with high degree of reconfigurability and are therefore favored techniques. The literature reports on various techniques to achieve particle separation, but these do not investigate the sensitivity of these methods or are difficult to compare due to the lack of figures of merit. In this paper, we present analytical and numerical sensitivity analysis of the time-of-flight and a phase-modulated sorting scheme against various extrinsic and intrinsic properties. The results reveal great robustness of the phase-modulated sorting method against variations of the flow rate or acoustic energy density, while the time-of-flight method shows lower efficiency drop against size and density variations. The results presented in this paper provide a better understanding of the two sorting methods and offer advice on the selection of the right technique for a given sorting application.

Most mobile phones today have capacitive microelectromechanical systems (MEMS) microphones that use either single or dual diaphragm. Methods to detect failures easily and non-invasively have become of critical importance for microphones mobile phone manufacturers as a basis for built-in self-test (BIST) and self-repair (BISR) strategies. In that regard, a four-layer framework is presented that includes lumped element modelling (LEM), failure mode simulation, failure mode discrimination and recovery. The frequency response of the microphone is taken as the main output to analyse. To experimentally validate this framework, this article provides a failure mode induction method based on bias voltage sweeping and four new techniques, based solely on acoustic measurements to discriminate the states of electrostatic capture for single diaphragm capacitive MEMS microphones. These include a) analysis of an acoustic signature that is unique to electrostatic capture based on cosine similarity analysis, b) -3 dB point measurement, C) +3 dB point measurement, and d) cluster analysis. Measurement of pull-in voltage and snapback voltage ranges is further demonstrated based on sensitivity measurements in laboratory conditions and response magnitude and noise power measurements in non-laboratory conditions. Up to 100% success rate in detecting electrostatic capture of diaphragm is reported for this type of device.

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