JOURNAL ARTICLE
A Novel Bootstrapped CMOS Switch with Minimized Sampling and Holding Error Using Sampling Window Error Analysis.
Published In: Journal of Circuits, Systems & Computers, 2024, v. 33, n. 15. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sharma, Buddhi Prakash; Mysakshi, Chandu; Kumar, Shivam; Gupta, Anu; Shekhar, Chandra 3 of 3
Abstract
This study proposes a novel 6-transistor bootstrapped switch with minimized sampling and holding error obtained through sampling window error analysis for SAR ADC design. The proposed switch design strategically mitigates channel charge injection and minimizes the input signal dependency of on-resistance by optimizing its sizing parameters. To counteract channel charge effects, dummy NMOS and PMOS components are judiciously employed, culminating in a substantial improvement in the effective number of bits (ENOB). The complete analysis of the proposed circuit is done using the Cadence Virtuoso SCL 0.18 μ m CMOS process. For a 51.514 kHz sinusoidal 1 V peak-to-peak differential input signal with a 1 MSPs clock speed, the proposed circuit achieves 2.0141 mV maximum sampling window error, 0.131 μ W power consumption, 84.67 dB signal-to-noise ratio (SNR), 84.67 signal-to-noise and distortion (SINAD) ratio and 86.02 dB spurious-free dynamic range (SFDR), which produces 13.77 bits ENOB. For the impacts of process variations and mismatch on switch performance, a comprehensive 500-point Monte Carlo (MC) simulation of the proposed bootstrap switch is conducted in this study. Post-layout results show that the proposed circuit is suitable for IoT applications. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Circuits, Systems & Computers. 2024/10, Vol. 33, Issue 15, p1
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2024
- ISSN:0218-1266
- DOI:10.1142/S0218126624502645
- Accession Number:180410017
- Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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