To be able to amplify an RF signal located at any of the supported cellular frequency bands, a wideband noise-canceling low-noiseamplifier (LNA) appears to be a good choice. As the receivers, later introduced in Chapter 5, are based on sampling the input charge, the RF amplifier needs to provide current rather than voltage, thus acting as a transconductance amplifier (TA) exhibiting a high output impedance compared to the input load of its subsequent stage. An LNTA (i.e., LNA+TA) could trivially be constructed by cascading LNA and TA (gm) stages. However, to improve noise and linearity, both of these circuits should be codesigned and tightly coupled. This chapter presents examples of state-of-the-art wideband noise-canceling LNTAs.

This chapter gives a short summary of mathematical instruments required to model sensor systems in the presence of both deterministic and random processes. The concepts are organized in a compact overview for a more rapid consultation, emphasizing the convergences between different contexts.

This chapter presents a general overview of sensor characterization from a system perspective, without any reference to a specific implementation. The systems are defined on the basis of input and output signal description and the overall architecture is discussed, showing how the information is transduced, limited, and corrupted by errors. One of the main points of this chapter is the characterization of the error model, and how this one could be used to evaluate the uncertainty of the measure, along with its relationship with resolution, precision and accuracy of the overall system. Finally, the quantization process, which is at the base of any digital sensor systems, is illustrated, interpreted, and included in the error model.

Photon transduction is a fundamental process of any optical detector or image sensor where the basic task is to estimate an average quantity of photons versus time and/or space. We start from basic physical phenomena of the optical transduction considering photon flux as an average quantity, disregarding the quantum mechanics characteristics of a single photon. Then, we investigate the role of noise in the transduction process to better assess design rules in electronic design of interfaces. As in the other transduction chapters, we treat only a very small part of existing optical sensor implementations to serve as examples of the application of the transduction principle.

Understanding the origin of noise is important because it gives hints on how to reduce its effects even from the electronic point of view. This chapter analyzes the physics background of some sources of random processes that are limiting sensing systems referred to as “thermal,” “shot,” and “flicker” noises. It also shows how thermal and shot noises are at the base of other observed electronic effects such as “kTC,” “phase,” and “current” noises. The discussion uses analogies between mechanical and electronic effects of thermal agitation. This is important not only for understanding the process but also to unify the model of noise in microelectromechanical sensor systems so as to use the same analysis framework.

This chapter is focused on the concepts of mechanical and thermal transduction related to the change of conductance and polarization in materials. Therefore, after an introduction on basic concepts, the transduction processes of piezoresistivity, piezoelectricity, and temperature effects on resistance are discussed. Finally, examples of applications of resistance sensors are given, focusing on some techniques to reduce errors due to influence variables.

This chapter treats two important steps in electronic sensor design. The first is the passage from functional blocks to lumped model electronic circuits. In this approach noise will be no more associated with functional blocks, but with circuit topology and electronic device elements. The second step is to analyze the effects of the readout mode on noise, emphasizing the differences between continuous and discrete-time approaches. Finally, we discuss some tradeoffs related to bandwidth and resolution in acquisition chains.

This chapter provides the essential concepts of compressive sensing (CS), also called compressed sensing, compressive sampling, or sparse sampling. A basic knowledge of signal processing is assumed. The treatment is rigorous but limited: more details can be found on the recommended textbooks given at the end of the chapter.

This chapter collects a series of problems and solutions spanning all the sections of this book.

The noise performance and the main characteristics of electronics devices and elementary building blocks have been discussed in earlier chapters. Here, more complex techniques for sensing interfaces are presented. Architectures tailored for specific cases such as resistive and capacitive sensing are analyzed. Furthermore, modulation, feedback, and time-to-digital techniques for signal detection are shown.

The purpose of this chapter is to set up the framework on which the book will be shaped up and it is intentionally based on informal descriptions of concepts. This is obviously a nonrigorous approach but is a fundamental step toward an abstraction process about artificial sensing: what are the ideas behind the general definition of sensors, their main performance limiting processes and essential tradeoffs. Using this inductive approach, we will first define concepts, leaving the formalization to the next chapters of the book. However, if the reader is facing this field for the first time, the argumentation could appear vague and fuzzy; therefore this first chapter should be read again after the rest of the book as the last one.

This chapter starts by first describing techniques to reduce errors. As far as the random ones are concerned, reduction approaches oriented to increase the signal-to-noise ratio on the spectrum domain and their strict relationship with sample averaging are discussed. Following, strategies for limitation of systematic errors are presented, especially based on the feedback concept. However, since the error reduction techniques allow several degrees of freedom, this chapter discusses the tradeoffs in optimizing sensing systems from the resolution, bandwidth, and power consumption point of view. More specifically, the resolution optimization of the sensing process is treated under the information theory point of view and the approach is extended to acquisition chains to understand the role of single building blocks.