Since the 1970s, a great deal of attention has been paid to the development of semiconductor-based biosensors because of the numerous advantages they offer, including high sensitivity, faster response time, miniaturization, and low-cost manufacturing for quick biospecific analysis with reusable features. Commercial biosensors have become highly desirable in the fields of medicine, food, and environmental monitoring as well as military applications, whereas increasing concerns about food safety and health issues have resulted in the introduction of novel legislative standards for these sensors. Numerous devices have been developed for monitoring biological processes such as nucleic acid hybridization, protein–protein interaction, antigen–antibody bonds, and substrate–enzyme reactions, just to name a few. Since the 1980s, scientific interest moved to the development of semiconductor-based devices, which also include integrated front-end electronics, such as the extended-gate field-effect transistor (EGFET) biosensor, one of the first miniaturized chemical sensors. This work is intended to be a review of the state of the art focused on the development of biosensors and chemosensors based on extended-gate field-effect transistor within the field of bioanalytical applications, which will highlight the most recent research reported in the literature. Moreover, a comparison among the diverse EGFET devices will be presented, giving particular attention to the materials and technologies.

Extended-gate field-effect transistor (EGFET) is an electronic interface originally developed as a substitute for an ion-sensitive field-effect transistor (ISFET). Although the literature shows that commercial off-the-shelf components are widely used for biosensor fabrication, studies on electronic interfaces are still scarce (e.g., noise processes, scaling). Therefore, the incorporation of a custom EGFET can lead to biosensors with optimized performance. In this paper, the design and characterization of a transistor association (TA)-based EGFET was investigated. Prototypes were manufactured using a 130 nm standard complementary metal-oxide semiconductor (CMOS) process and compared with devices presented in recent literature. A DC equivalence with the counterpart involving a single equivalent transistor was observed. Experimental results showed a power consumption of 24.99 mW at 1.2 V supply voltage with a minimum die area of 0.685 × 1.2 mm². The higher aspect ratio devices required a proportionally increased die area and power consumption. Conversely, the input-referred noise showed an opposite trend with a minimum of 176.4 nV_rms over the 0.1 to 10 Hz frequency band for a higher aspect ratio. EGFET as a pH sensor presented further validation of the design with an average voltage sensitivity of 50.3 mV/pH, a maximum current sensitivity of 15.71 mA^1/2/pH, a linearity higher than 99.9%, and the possibility of operating at a lower noise level with a compact design and a low complexity.

The industrial revolution and rapid urbanization of human society have put a great strain on the resource consumption and environmental issues. The concept of a smart city with advanced technology can play a vital role to overcome these challenges and make life more sustainable and environment greener. Sensing technology has an intense impact in this regard starting from smart design of the infrastructure to autonomous control and monitoring of the overall system. This paper presents an overview of the state-of-the-art sensor technologies that can play a vital role in the design of a smart city. This paper is aimed to provide a brief review of sensing technology on some selected platforms such as environmental monitoring, agriculture, and food safety as well as in security. By discussing the emergence of the low-power smart sensor and the associated challenges, this paper attempts to provide a future research direction.

The paper presents development of a smart infant monitoring system using multiple non-invasive sensors to detect various physiological functions. The system can evaluate different physiological activities such as respiration, movement, noise, position, as well as ambient temperature, and humidity. By processing the acquired data from different sensor modules, the system can generate alarm signals for adverse situations such as the occurrence of apnea, seizure, or noisy and uncomfortable environmental conditions. The system will also be able to detect critical respiratory conditions by analyzing breathing data and saturated blood oxygen level (SpO2) using machine learning (ML) models such as neural networks. The proposed system allows the caregiver to monitor the condition of the patient from a remote location by implementing wireless communication with a remote computer or a cell phone.

This chapter covers different methods of semiconductor device modeling for electronic circuit simulation. It presents a discussion on physics-based analytical modeling approach to predict device operation at specific conditions such as applied bias (e.g., voltages and currents); environment (e.g., temperature, noise); and physical characteristics (e.g., geometry, doping levels). However, formulation of device model involves trade-off between accuracy and computational speed and for most practical operation such as for SPICE-based circuit simulator, empirical modeling approach is often preferred. Thus, this chapter also covers empirical modeling approaches to predict device operation by implementing mathematically fitted equations. In addition, it includes numerical device modeling approaches, which involve numerical device simulation using different types of commercial computer-based tools. Numerical models are used as virtual environment for device optimization under different conditions and the results can be used to validate the simulation models for other operating conditions.