This work introduces SENSOTERIC, a multi-partner project that aims at leveraging the properties of emerging Reconfigurable Field Effect Transistors (RFETs) to develop a sensor platform. RFETs will be used for a generic sensor interface and for a dedicated transducer element. In the first case, our goal is to develop an analog front-end interface that can be tuned at runtime to adapt to different environmental conditions and be used in a broad spectrum of applications. This feature shall be enabled by the polarity-control and negative differential resistance characteristics of the reconfigurable devices employed, that are co-integrable on industrial CMOS processes such as 22 nm FDSOI. In the second case, we want to exploit the intrinsic nature of these doping-free devices to yield better 1/f noise performances when compared to classic CMOS transducers. Moreover, the presence of un-gated areas on top of the channel of these devices makes them the perfect candidates to be functionalized. In this early-stage overview of the project, we will introduce the key features and the vision that make SENSOTERIC a unique contribution towards smart sensing solutions in environmental monitoring and healthcare.

Electrochemical devices and systems are significant for the development of therapeutic drug monitoring (TDM) and personalized therapy. However, electrochemical sensors are usually not that much selective. Therefore, innovative machine learning (ML) approaches are now required to improve the selectivity at system level based on cyclic voltammograms (CV) obtained from electrochemical sensors in order to assure the quantification of several different drugs simultaneously present into the blood of patients. Based on an Artificial Neural Network (ANN), this paper proposes a novel model TwoWayANN along with an adaptive weighted cross-entropy loss (AWCEL) to address drugs interaction effectively and decline the quantification error range. The simulated dataset of etoposide (ETO) and methotrexate (MTX), proposed here as model drugs, is demonstrated to validate the efficacy of our proposed method. Our TwoWayANN model achieves the accuracy at 100% and 99.35% for ETO and MTX respectively within the error range of ±5μM. Our results are important for the development of point-of-care systems for applications in personalized therapy.

Over the years, several clock and data recovery architectures have been proposed for wireless Amplitude Shift Keying (ASK) transmitted signals. State-of-the-art architectures mainly rely on synchronous phase-locked loop circuits or self- sampling systems, both resulting in large area consumption. This work presents a novel CMOS architecture for Clock and Data Recovery (CDR) in miniaturised and wirelessly powered implants. The proposed CDR architecture works at 433.92 MHz and includes: an ASK-demodulator, an on-chip oscillator, a power-on-reset, a control and a recovering block operating in feedback-loop. The ASK-demodulator works for a data rate as high as 6 Mbps and a modulation index in the range of 9-30%. A novel communication protocol is presented for a separated clock and data transmission. The entire CDR architecture occupies$17 \times 89 \mu m^{2}$and consumes$15.01 \mu W$while operating with a clock rate of 6 Mbps.

We present here the first ever-reported direct computation of the cancer risk on cancer markers simultaneously detected by memristors. The novel approach in cancer diagnostics here proposed is based on the fusion of the three actions of sensing, computing and memory in a single-kind of device. So, this paper proposes the new concept of in-memory sensing as a disruptive approach in edge-computing. The concept is demonstrated by showing a well-known case in cancer diagnostics: the estimation of the risk of prostate cancer based on the simultaneous measure of Prostate Specific Antigen (PSA) and its Membrane isoform (PSMA).

With rapid development of Internet of Things, there is high demand for miniaturized biochemical sensors, which enable a digital-controlled connection and portable integration. In this work, we report an ASIC interface for capacitive micromachined ultrasonic transducers (CMUTs), offering multi-channel monitoring of frequency shifts and supplying high-voltage (HV) source. Previously reported CMUTs-based biochemical sensors required a peripheral power source to provide a DC driving voltage (e.g.,10-50 V), which was higher than the common circuit supply (e.g., 1.2-5 V). This resulted in additional circuit footprints and parasitic effects, especially for high frequency. Instead, we proposed an ASIC interface designed through TSMC $0.18-\mu{m}$ HV BCD process with a die size of 2×2 mm2. Four-channel oscillators are proposed to track frequency shifts of CMUT resonators with a resonant frequency of 1.8MHz, and a start-up period up to $100\mu$ s and a phase noise of −38.73dBc/Hz at low offset of 100 Hz were observed, while consuming an average total power of $142.12\mu\mathrm{W}$ in continuous mode. A PMOS-based HV charge pump is presented to generate a voltage boosting of ~50 V with a 1.8-V input source, which enables tuning clock signals by implementing four-parallel ring oscillators with the frequency of 20-50 MHz. The proposed ASIC integrated with CMUTs is a molecules.

Multi-ion potentiometric sensing becomes challenging for mixed-ion samples in which interfering electrolytes significantly alter the response of each single sensor. Therefore, an emulator based on the phase-boundary potential model is proposed to simulate multi-ion sensing in presence of interference. It serves as investigation tool for understanding the impact of sensor selectivity and interferent ions in the sensor response. Moreover, the emulator is used to design mixed-ion synthetic dataset, following a multi-factorial design of ion mixtures with orthogonal arrays. Such large dataset is suitable for data-intensive learning algorithms used in multivariate calibration of potentiometric sensor arrays.

In this work, a complete study is carried out for the optimised, direct and catalyst-free growth of vertically-stacked graphene-based structures targeting at improved performance drug monitoring. The nanostructures, ultimately forming a honeycomb network on the substrate, are fabricated by the implementation and comparison of seven combinations of growth conditions on both Si and SiO2substrates. Pivotal features characterising the nanostructures i.e. layer thickness, sheet resistance, surface morphology and sensing performance are considered for verifying the quality and properties of the resulted graphene-based electrodes. The graphene-based sensing platform demonstrating optimum structural and electrochemical performance is finally implemented for drug screening showing high efficiency for the detection of a chemotherapeutic compound at low concentrations.

Medical Internet of Things (IoT) solutions are already paving the way towards the realization of Smart Hospitals. Following this trend, we propose an IoT cloud-based network for anesthesia on-line monitoring. This architecture allows the anesthesiologist to remain simultaneously connected to all the patients under his/her responsibility. To do so, the Android app, running on the anesthesiologists tablet/smartphone, plays the central role of the architecture. From one side, it receives data from the Therapeutic Drug Monitoring (TDM) system on the patient; on the other side, it pushes data to the cloud, exploiting the Pryv middleware solution, enabling teleconsulting. All the communications exploits Wi-Fi, therefore, all data are also stored locally in the physical devices's memory to face Internet unavailability. The flexibility and the portability of our monitoring architecture ensure the possibility to interface with any medical device which can wirelessly send the measured data. Therefore, a variety of medical monitoring applications can also be addressed.

Connected medical devices are already paving the way towards the realization of Smart Hospitals where patient care is improved thanks to Internet of Things (IoT) solutions. Following this trend, in this paper we propose an IoT cloud-based network for anesthesia on-line monitoring. This architecture allows the anesthesiologist to remain simultaneously connected to all the sedated patients through an Android app. Moreover, medical data from the patients can be shared on a cloud solution accessible by a web application enabling teleconsulting. Hence, by accessing the cloud, medical specialists can consult the shared data from everywhere and at any time. The flexibility and the portability of our monitoring architecture ensure the possibility to interface with any medical device which can wirelessly send the measured data. Therefore, other medical monitoring applications can also be addressed.

Memristive biosensors have been proved as excellent candidates for ultrasensitive biosensing. In this work, a novel portable bio-detection system based on memristive biosensors is designed, developed and tested for providing a significantly fast, automatic and simultaneous sensing output of multiple memristive biosensors on a single chip. The suggested compact and independent bio-sensing prototype is achieved through the realization of an electronic board designed for addressing the specifications of the memristive biosensor signal acquisition. Memristive bio-sensing-chips are specially designed and fabricated as well. The system was tested for successfully sensing of Prostate Specific Antigen, one of the main biomarkers of prostate cancer, at fM concentrations. Overall, this novel scheme resembles to a memristive-biosensing-kit approach, paving the way for fast and ultrasensitive PoC (point-of-care) devices.

Induction of anesthesia with propofol is a largely adopted technique in hospital environments. The correct dosage of this compound is essential to avoid under- or over-anesthesia that may result in serious side-effects. Therefore, in clinical settings, long-term monitoring of propofol is of great importance. To this aim, in this work, we present the design and the validation of a custom-made, low-cost and portable Point-of-Care (PoC) system based on electrochemical detection for propofol monitoring. Fouling phenomenon due to phenolic oxidation of propofol has been overcome by adopting a Pencil Graphite Electrode (PGE) 3H as sensor. The validation of the system focused on testing the measurement speed through scan-rate analysis, which is important due to the fast clearance of propofol ; and interference study with Paracetamol (APAP), since it is an analgesic compound frequently administered with propofol in medical practices.

Reducing sensing time is one major concern in clinical diagnostics. In the present work, a robust measurement system is developed aiming at the faster and easier signal acquisition of memristive biosensors. Sensing chips consisting of nanofabricated silicon wires exhibiting memristive electrical response and metallic extension electrodes allowing an integrated measurement procedure are designed and fabricated. Furthermore, the electrical response of these particular nanofabricated structures is for the first time acquired using an embedded-system-based measurement front-end. The suggested prototype significantly simplifies the measurement procedure and provides conveniently the response signal of the devices. Such optimized co-design of memristive biosensors with electronic platforms hold great promise for PoC (point-of-care) applications.

A fully-integrated differential biosensing platform on CMOS is presented for miniaturized enzyme-based electrochemical sensing. It enables sensor background current elimination and consists of a differential sensor array and a differential readout IC (DiRIC). The sensor array includes a four-electrode sensor for amperometric electrochemical sensing, as well as a differential ISFET-based pH sensor to calibrate the biosensors. The ISFET is biased in weak inversion and co-designed with DiRIC to enable pH measurements from 1 to 14 with resolution of 0.1 pH. DiRIC enables differential current measurement in the range of ±100 μA with more than 120dB dynamic range.

As metabolic pathways are highly variable among different patients, then an accurate and continuous monitoring of the personal response to drug treatments is essential especially with critical medications, e.g. anesthetic and anti-cancer cocktails. Currently, for anesthesia monitoring, there are no fully mature point-of-care bio-sensing systems. Indeed, it is still evaluated by mathematical models and with indirect parameters like Bispectral Index, which can not provide the actual concentration in blood. The aim of this paper is to investigate solutions for developing a portable system able to monitor simultaneously several different drugs. For this purpose, Cyclic Voltammetries with an anesthetic product and therapeutic compounds were performed for the validation of the system. A multichannel potentiostat has been realized with Off-The-Shelf Components (COTS). Then, results from electrochemical acquisitions on GC SPEs show that the proposed circuitry is suitable for this aim and they prove its high flexibility to develop portable systems for continuous monitoring of anesthetics and therapeutic compounds.

Integration of nanowire structures in more complex platforms combining microfluidics and multiplexing aspects may complex the electrical readout process. In the present work, fabrication of Aluminium (Al) lines for electrical connections is attempted followed by their integration to nanofabricated Memristive Biosensors that target at the label-free detection of cancer biomarkers. In addition a computational study is carried out investigating the impact of the resistance introduced by the additional Al connections to the electrical response of the Memristive Biosensors. Overall, the present study explores the restrictions and the possibilities of the Memristive Biosensors' integration with additional elements that would allow effective readout in more complex configurations.

The development of fast and mobile drug detection is an important aspect of personalized medicine. It enables the quick assessment of inter-individual differences in drug metabolism and corresponding adjustments of the dose. Recent developments of amperometric biosensors using cytochrome P450 (CYP) show great promise, by lowering the detection limit to physiological range for several drugs via the usage of Multi Walled Carbon Nanotubes (MWCNT). The next challenge is to develop algorithms for processing the resulting sensor data compatible with low-power hardware, which would allow the development of portable battery-powered devices. In this work we pursue a novel approach to this problem. Here we provide a proof of principle by demonstrating how sensor data could be analyzed using a conventional multi-layer perceptron network with error-backpropagation.

A full remotely powered system for metabolism monitoring of free-moving mice is presented here. The fully implantable sensing platform hosts two ASICs, one off-the-shelf micro-controller, four biosensors, two other sensors, a coil to receive power, and an antenna to transmit data. Proper enzymes ensure specificity for animal metabolites while Multi-Walled Carbon Nanotubes ensure the due sensitivity. The remote powering is indeed provided by inductive coils located under the floor of the mouse' cage. Two different approaches where investigated to ensure freedom of movement to the animal. The application to studies for personalized medicine is demonstrated by showing continuous monitoring of both glucose and paracetamol.

State-of-the-art techniques for measuring and monitoring gene level expression rely on messenger RNA (mRNA) extraction and quantification, usually based on the concept of reverse transcription polymerase chain reaction. In this paper, we take advantage of capabilities of image segmentation algorithms for monitoring target cell surface biomarkers using immunofluorescence microscopy. As a case study, we are looking at the expression level of toll-like receptor 2 (TLR2) proteins on Caco-2 intestinal cells after stimulation with lipopolysaccharide. The goal of this paper is to identify the segmentation algorithm which provides the best correlation between the pixel intensities of fluorescent images and quantified TLR2 mRNA. Three image segmentation algorithms are considered in this study for processing the fluorescent images acquired using a low-cost CMOS sensor. We conclusively show the existence of a proper segmentation algorithm from which we can extract results that are heavily correlated with TLR2 mRNA quantifications. The obtained results open possibilities for cost-effective and real-time monitoring of biomarkers with applications in embedded or lab-on-chip systems.

In this paper we propose CMOS-compatible Memristive-Biosensors as label-free, highly sensitive sensors for in-air detection of Vascular Endothelial Growth Factor (VEGF) molecules. The memristive behavior of the fabricated devices is strongly affected by molecules in proximity of the wire surface. In this paper, we demonstrate the reproducibility of the measurement based on the memristive voltage gap. We also show the successful sensing of femto-molar amounts of VEGF. Specifically, we demonstrate a correlation between the decreasing behavior of the voltage gap and the increasing concentrations of VEGF. The voltage gap dependence on the pH of the initial solution is also shown as a further proof of the ionic interactions occurring at the SiNW surface. All measurements are performed in air, under controlled humidity; this makes our approach more sensitive thanks to the lowered Debye screening effect of counterions.

A power delivery system for implantable biosensors is presented. The system, embedded into a skin patch and located directly over the implantation area, is able to transfer up to 15 mW wirelessly through the body tissues by means of an inductive link. The inductive link is also used to achieve bidirectional data communication with the implanted device. Downlink communication (ASK) is performed at 100 kbps; uplink communication (LSK) is performed at 66.6 kbps. The received power is managed by an integrated system including a voltage rectifier, an amplitude demodulator and a load modulator. The power management system is presented and evaluated by means of simulations.

High quality CMOS image sensors are of great importance for LoC - Lab-on-Chip devices based on optical measurements. The main target in these devices is to minimize the cost and area while achieving a good resolution. The performance parameters of image sensor pixels and CMOS compatible photodiodes depend on the size, type and the geometry of the photodiode layout and varies for each technology. In this study, we present a comparative analysis of CMOS compatible photodiode types at different areas. The results have shown n-well/p-sub type photodiode with 5×5 μm2diffusion area achieves the highest sensitivity (69.81 × 1012V.s-1.cm-2/W.cm-2) and with 40 × 40 μm2diffusion area, highest SNR - Signal-to-Noise Ratio (72.26dB) at 630 nm, while the p+/n-well/p-sub type photodiode with 40 × 40 μm2diffusion area results in highest responsivity (0.466 A. cm-2/W.cm-2) at the same wavelength.

Biosensors are heterogenous devices, incorporating biological structures combined with electronics, optical or other readout systems. They have been developed for detecting different biomolecules and/or pathogens and represent a key technology for advanced and point-of-care diagnostics as well as patient monitoring. In this paper we present a systematic classification of biosensors described in literature, particularly focusing on nanotechnology-based sensing. Then, we present our approach to develop electrochemical biosensors for measuring metabolites and anticancer drugs, based on a platform for multiple target detection. This platform is modular and achieves a clear separation between the chemical and the electrical components, thus easing design and manufacturing. It shows superior performance thanks to the excellent properties of electron transfer and selectivity showed by enzymes immobilized on carbon nanotubes.

In biological applications and systems where even the smallest details have a meaning, CCD cameras are mostly preferred and they hold most of the market share despite their high costs. In this paper, we propose a custom-designed CMOS camera to compete with the default CCD camera of an inverted microscope for fluorescence imaging. The custom-designed camera includes a commercially available mid-performance CMOS image sensor and a Field-Programmable Gate Array (FPGA) based hardware platform (FPGA4U). The high cost CCD camera of the microscope is replaced by the custom-designed CMOS camera and the two are quantitatively compared for a specific application where an Estrogen Reception (ER) expression in breast cancer diagnostic samples that emits light at 665nm has been imaged by both cameras. The gray-scale images collected by both cameras show a very similar intensity distribution. In addition, normalized white pixels after thresholding resulted in 4.96% for CCD and 3.38% for CMOS. The results and images after thresholding show that depending on the application even a mid-performance CMOS camera can provide enough image quality when the target is localization of fluorescent stained biological details. Therefore the cost of the cameras can be drastically reduced while benefiting from the inherent advantages of CMOS devices plus adding more features and flexibility to the camera systems with FPGAs.

The objective of this work is the systematic study of the use of electrochemical readout for advanced diagnosis and drug monitoring. Whereas to date various electrochemical principles have been studied and successfully tested, they typically operate on a single target molecule and are not integrated in a full data analysis chain. The present work aims to view various sensing approaches and explore the design space for integrated realization of multi-target sensors and sensor arrays.

Memristors are passive circuit elements that behave as resistors with memory. The recently illustrated experimental realization of memristive behaviour of Polysilicon Nanowires has triggered interest in this concept, which is promising to a wide variety of application areas that include neuromorphic circuits. In order to progress with practical implementations that use this technology we need to expand our understanding of the conduction mechanisms in these structures and of the underlying relationship between device behavior and process manufacturing parameters. In this paper we explore these mechanisms through detailed simulation, which includes model calibration and correlation with experimental results. Through fitting of the test results we identify a unique set of density of states that characterize the particular technology implemented.

Bio-detection specially dedicated to distributed diagnostics is emerging as a quite important application for Nano-bioelectronics. Bio-detection is required to be highly sensitive in order to succeed in sensing small amount of bio-markers in patient's blood sample. Carbon Nanotubes (CNTs) provides devices in the scale of the target molecules, thereby opening up possibilities to sense few bio-markers. Moreover, bio-detection is also required to be highly specific in order to succeed in disease discrimination. FET technology provides control in multi-panel biochip to enhance specificity. The aim of the present paper is to design an array of Carbon Nanotube Field Effect Transistors (CNFETs) to provide nano-biosensing based on cytochromes P450. In particular, a novel CNFET biosensor array design is proposed which is robust to the imperfections in the CNTs thereby achieving high level of sensitivity. The proposed CNFET based design assures the improved specificity by means of a multi-gate controller at the nano-scale. The proposed application in distributed diagnostics is on detection of drugs in multi-components samples by multiplexing different P450 probes.

This paper reports on the memory and memristive effects of Schottky barrier field effect transistors (SBFET) with gate-all-around (GAA) configuration and Si nanowire (SiNW) channel. Similar behavior has also been investigated for SBFETs with poly-Si nanowire (poly-SiNW) channel in back-gate configuration. The memristive devices presented here have the potential of a very high integration density, and they are suitable for hybrid CMOS co-fabrication with a CMOS-compatible process. We show that 2 different regimes are possible, making these devices suitable either for volatile ambipolar memory or resistive random access memory (RRAM) applications. In addition, frequency- and amplitude- dependence of the memristive behavior are reported.