Precision Voltage Sensing in Deep Sub-micron and Its Challenges

Ivan O'Connell; Subhash Chevella; Gerardo Molina Salgado; Daniel O'Hare

doi:10.1007/978-3-030-91741-8_9

Precision Voltage Sensing in Deep Sub-micron and Its Challenges

Ivan O'Connell
, Subhash Chevella
, Gerardo Molina Salgado
, Daniel O'Hare

University College Cork

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

Abstract

Realising high precision Analog to Digital Converters (ADCs) in 28 nm and below is a significant challenge. Traditional Sigma Delta architectures are not compatible with reduced voltage rails and transistor gain. While Successive Approximation Register (SAR) ADCs have scaled well with technology nodes, they have struggled to consistently and reliably achieve 12+ bit performance. A new emerging architecture, the Noise-Shaped SAR ADC, leverages the advantages of both architectures. However, it is the author's view that there are still many challenges that need to be addressed which are discussed here to realise a high precision converter in these deep sub-micron nodes.

Original language	English
Title of host publication	Analog Circuits for Machine Learning, Current/Voltage/Temperature Sensors, and High-speed Communication
Subtitle of host publication	Advances in Analog Circuit Design 2021
Publisher	Springer International Publishing
Pages	137-163
Number of pages	27
ISBN (Electronic)	9783030917418
ISBN (Print)	9783030917401
DOIs	https://doi.org/10.1007/978-3-030-91741-8_9
Publication status	Published - 24 Mar 2022

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10.1007/978-3-030-91741-8_9

Cite this

O'Connell, I., Chevella, S., Salgado, G. M., & O'Hare, D. (2022). Precision Voltage Sensing in Deep Sub-micron and Its Challenges. In Analog Circuits for Machine Learning, Current/Voltage/Temperature Sensors, and High-speed Communication: Advances in Analog Circuit Design 2021 (pp. 137-163). Springer International Publishing. https://doi.org/10.1007/978-3-030-91741-8_9

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Precision Voltage Sensing in Deep Sub-micron and Its Challenges. / O'Connell, Ivan; Chevella, Subhash; Salgado, Gerardo Molina et al.
Analog Circuits for Machine Learning, Current/Voltage/Temperature Sensors, and High-speed Communication: Advances in Analog Circuit Design 2021. Springer International Publishing, 2022. p. 137-163.

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

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PY - 2022/3/24

Y1 - 2022/3/24

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Precision Voltage Sensing in Deep Sub-micron and Its Challenges

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