In this chapter, we describe four realized examples of discrete-time receivers that are largely based on the architecture and circuitryintroduced in previous chapters. Starting with a commercial DT receiver designed for GSM single-chip radios, which introduces the novel low-pass IIR filter, we then continue with three highly reconfigurable superheterodyne receivers that employ the complex IQ charge-sharing band-pass filter (BPF) for image rejection.

One of the main building blocks in a receiver is a low-pass filter (LPF) used at the baseband. This block is responsible for selecting the desired channel. In zero-IF receivers, this block is placed directly after the RF downconversion mixer. In a high-IF receiver,the LPF is required after a second downconversion from the IF to baseband. In addition to wireless communication applications, integrated LPFs are the key building blocks in various other types of applications, such as hard disk drive readchannels,videosignalprocessing,smoothingfilteringinaDAC,andantialiasingfilteringbeforea sampling system. The noise of these filters is one of the key system-level concerns. This noise can be usually traded off with the total filter capacitance and, consequently, total power and area. Therefore, for a given system-level noise budget, a filter with a lower noise coefficient reduces the area and power consumption. On the other hand, the linearity of the filter should be high enough to maintain the fidelity of the wanted signal.

We start the book with the basics. In this chapter, we first present the motivation and fundamentals of discrete-time (DT) radio-frequency (RF) signal processing, and an overview of zero/low intermediate-frequency (IF) and superheterodyne receiver architec-tures. Then, different sampling schemes present in the state-of-the-art zero-IF DT receivers are studied using a simplified DT receiver. At the end, a 4×-sampling concept is introduced for use in DT high-IF receivers.

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.