Platforms that offer massively parallel, label-free biosensing can, in principle, be created by combining all-electrical detection with low-cost integrated circuits. Examples include field-effect transistor arrays, which are used for mapping neuronal signals and sequencing DNA. Despite these successes, however, bioelectronics has so far failed to deliver a broadly applicable biosensing platform. This is due, in part, to the fact that d.c. or low-frequency signals cannot be used to probe beyond the electrical double layer formed by screening salt ions, which means that under physiological conditions the sensing of a target analyte located even a short distance from the sensor (∼1 nm) is severely hampered. Here, we show that high-frequency impedance spectroscopy can be used to detect and image microparticles and living cells under physiological salt conditions. Our assay employs a large-scale, high-density array of nanoelectrodes integrated with CMOS electronics on a single chip and the sensor response depends on the electrical properties of the analyte, allowing impedance-based fingerprinting. With our platform, we image the dynamic attachment and micromotion of BEAS, THP1 and MCF7 cancer cell lines in real time at submicrometre resolution in growth medium, demonstrating the potential of the platform for label/tracer-free high-throughput screening of anti-tumour drug candidates.

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We propose a new approach to describe in commercial TCAD the chemical reactions that occur at dielectric/electrolyte interface and make the ion sensitive FET (ISFET) sensitive to pH. The accuracy of the proposed method is successfully verified against the available experimental data.\ud We demonstrate the usefulness of the method by performing, for the first time in a commercial TCAD environment, a full 2-D analysis of ISFET operation, and a comparison between threshold voltage and drain current differential sensitivities in the linear and saturation regimes. The method paves the way to accurate\ud and efficient ISFET modeling with standard TCAD tools

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Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.