Sensitivity, Noise and Resolution in a BEOL-Modified Foundry-Made ISFET with Miniaturized Reference Electrode for Wearable Point-of-Care Applications

Sensor technology usage in orthopedic trauma

Medicine in general is quickly transitioning to a digital presence. Orthopaedic surgery is also being impacted by the tenets of digital health but there are also direct efforts with trauma surgery. Sensors are the pen and paper of the next wave of data acquisition. Orthopaedic trauma can and will be part of this new wave of medicine. Early sensor products that are now coming to market, or are in early development, will directly change the way we think about surgical diagnosis and outcomes. Sensor development for biometrics is already here. Wellness devices, pressure, temperature, and other parameters are already being measured. Data acquisition and analysis is going to be a fruitful addition to our research armamentarium with the volume of information now available. A combination of broadband internet, micro electrical machine systems (MEMS), and new wireless communication standards is driving this new wave of medicine. The Internet of Things (IoT) [1] now has a subset which is the Internet of Medical Devices [2-5] permitting a much more in-depth dive into patient procedures and outcomes. IoT devices are now being used to enable remote health monitoring, in hospital treatment, and guide therapies. This article reviews current sensor technology that looks to impact trauma care.

Multiscale simulation analysis of passive and active micro/nanoelectrodes for CMOS-based in vitro neural sensing devices

Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for in vitro studies fabricated with modern (CMOS-based) micro-nano technology, this paper presents a mixed-mode device-circuit numerical-analytical multiscale and multiphysics simulation methodology to describe the neuron-sensor coupling, suitable to derive useful design guidelines. A few representative structures and coupling conditions are analysed in more detail in terms of the most relevant electrical figures of merit including signal-to-noise ratio. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Field-Effect Transistor-Based Biosensors for Environmental and Agricultural Monitoring

The precise monitoring of environmental contaminants and agricultural plant stress factors, respectively responsible for damages to our ecosystems and crop losses, has nowadays become a topic of uttermost importance. This is also highlighted by the recent introduction of the so-called "Sustainable Development Goals" of the United Nations, which aim at reducing pollutants while implementing more sustainable food production practices, leading to a reduced impact on all ecosystems. In this context, the standard methods currently used in these fields represent a sub-optimal solution, being expensive, laboratory-based techniques, and typically requiring trained personnel with high expertise. Recent advances in both biotechnology and material science have led to the emergence of new sensing (and biosensing) technologies, enabling low-cost, precise, and real-time detection. An especially interesting category of biosensors is represented by field-effect transistor-based biosensors (bio-FETs), which enable the possibility of performing in situ, continuous, selective, and sensitive measurements of a wide palette of different parameters of interest. Furthermore, bio-FETs offer the possibility of being fabricated using innovative and sustainable materials, employing various device configurations, each customized for a specific application. In the specific field of environmental and agricultural monitoring, the exploitation of these devices is particularly attractive as it paves the way to early detection and intervention strategies useful to limit, or even completely avoid negative outcomes (such as diseases to animals or ecosystems losses). This review focuses exactly on bio-FETs for environmental and agricultural monitoring, highlighting the recent and most relevant studies. First, bio-FET technology is introduced, followed by a detailed description of the the most commonly employed configurations, the available device fabrication techniques, as well as the specific materials and recognition elements. Then, examples of studies employing bio-FETs for environmental and agricultural monitoring are presented, highlighting in detail advantages and disadvantages of available examples. Finally, in the discussion, the major challenges to be overcome (e.g., short device lifetime, small sensitivity and selectivity in complex media) are critically presented. Despite the current limitations and challenges, this review clearly shows that bio-FETs are extremely promising for new and disruptive innovations in these areas and others.

Fabrication and Characterization of In0.9Ga0.1O EGFET pH Sensors

In this study, the In0.9Ga0.1O sensing membrane were deposited by using the RF magnetron sputtering at room temperature and combined with commercial MOSFETs as the extended gate field effect transistor (EGFET) pH sensors. The sensing performance of the In0.9Ga0.1O EGFET pH sensors were measured and analyzed in the pH value of range between 2 to 12. In the saturation region, the pH current sensitivity calculated from the linear relationship between the IDS and pH value was approximately 56.64 μA/pH corresponding to the linearity of 97.8%. In the linear region, the pH voltage sensitivity exhibited high sensitivity and linearity of 43.7 mV/pH and 96.3%, respectively. The In0.9Ga0.1O EGFET pH sensors were successfully fabricated and exhibited great linearity. The analyzed results indicated that the In0.9Ga0.1O was a robust material as a promising sensing membrane and effectively used for pH sensing detection application.

Integration of Ultra-Low Volume Pneumatic Microfluidics with a Three-Dimensional Electrode Network for On-Chip Biochemical Sensing

This paper reports a novel miniaturized pseudo reference electrode (RE) design for biasing Ion Sensitive Field Effect Transistors (ISFETs). It eliminates the need for post-CMOS processing and can scale up in numbers with the CMOS scaling. The presented design employs silane-mediated transfer of patterned gold electrode lines onto PDMS microfluidics such that the gold conformally coats the inside of microfluidic channel. Access to this electrode network is made possible by using “through-PDMS-vias” (TPV), which consist of high metal-coated SU-8 pillars manufactured by a novel process that employs a patterned positive resist layer as SU-8 adhesion depressor. When integrated with pneumatic valves, TPV and pseudo-RE network were able to bias 1.5 nanoliters (nL) of isolated electrolyte volumes. We present a detailed characterization of our pseudo-RE design demonstrating ISFET operation and its DC characterization. The stability of pseudo-RE is investigated by measuring open circuit potential (OCP) against a commercial Ag/AgCl reference electrode.

Stochastic Time Response and Ultimate Noise Performance of Adsorption-Based Microfluidic Biosensors

In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption-desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption-desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor's maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors' noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.