Автор: Pieter Harpe, Andrea Baschirotto, Kofi A.A. Makinwa
Издательство: Springer
Год: 2023
Страниц: 345
Язык: английский
Формат: pdf (true)
Размер: 23.5 MB
This book is based on the 18 tutorials presented during the 30th workshop on Advances in Analog Circuit Design. Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, with specific contributions focusing on analog circuits for machine learning, current/voltage/temperature sensors, and high-speed communication via wireless, wireline, or optical links. This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.
To determine and optimize the electrochemical sensor interface circuit specifications, as well as to propose novel topologies, a comprehensive understanding of its equivalent model is necessary. This chapter provides a detailed analysis of the sensor’s electrode-electrolyte interface and illustrates the electrical modeling of both two-electrode and three-electrode electrochemical systems. Furthermore, several realistic design assumptions and concerns are discussed, along with examples of typical electrochemical sensor interfaces, namely, potentiostats and current readout circuits. In conclusion, challenges and future trends are discussed for both the sensor modeling and the sensor interface, with the intention of researching and presenting new opportunities and future advances.
CMOS biosensors hold great promise for the high volume/low-cost scalability of molecular diagnostics. However, despite many publications in this area over the past decades, mainstream consumer adoption examples are limited. This chapter explores some of these biosensor opportunities and inherent challenges. It begins with an introduction to molecular detection basics and reviews current laboratory and point-of-care detection methods. Some historical biosensor approaches and recent CMOS biosensor examples are reviewed.
The noise-shaping successive approximation (SAR) is only a decade old but has quickly become a dominant ADC architecture. A noise-shaping SAR ADC combines the advantages of SAR and sigma-delta architectures. Noise-shaping SAR is more energy efficient than sigma-delta and better suited to higher resolution than SAR. Noise-shaping SAR ADCs are also very compact. The first noise-shaping SAR ADCs were limited to moderate resolution and moderate speed. Recent work applies high-order noise shaping to extend the SNR to audio performance levels. At the other extreme, the interleaving of noise-shaping SAR effectively tackles the speed bottleneck. Noise-shaping SARs are also valuable in hybrid ADCs. As a backend quantizer, a noise-shaping SAR very efficiently increases the order of a continuous-time sigma-delta.
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