Year: 2024
Type: article
Abstract: This paper introduces a fully differential asynchronous successive approximation register analog-to-digital converter (SAR ADC) designed for biomedical signal processing. By extending the tracking time and utilizing fully differential inputs in the analog front-end circuit, the signal-to-noise ratio is enhanced in the system. Using an asynchronous clock can reduce power consumption across a wider range of sampling frequencies. In comparison to conventional architecture in high-speed SAR ADC, using an internal clock generator can operate at lower frequencies. A fully differential input can eliminate the DC offset of the analog front-end circuit and reduce the adverse effects of process variation, voltage variation, and temperature variation. The chip is implemented by TSMC 0.18 μm complementary metal-oxide-semiconductor (CMOS) technology, and the chip area is 0.680 mm2 (including ESD I/O PAD). At a 1.2 V supply, the maximum sampling rate is 10 Kilo Samples per second (KSps). The implemented ADC has an 11-bit resolution, while the input voltage range is 300∼900 mV. The total power consumption is 1.7 μW, with the core power consumption at 932 nW.
Source: Electronics
Authors: Muh-Tian Shiue, Yu-Fan Lo, Chih-Yao Jung
Institution: National Central University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Awards: Develop Deep Learning Techniques for Next Generation beyond Gigabit Ethernet Transceiver( I ), Chip Design and Implementation of High Enerigy-Efficiency Reconfigurable Neuromorphic Computing for Deep Learning Appications, Design and Implementation of Massive Multiple-Input Multi-Output Transceiver for Vi Sible Light Communication System Based on Spatial Modulation Technique, Analog Echo Canceller Chip Design for Multi-Gigabit Ethernet Transceiver Using Deep Learning Techniques, Develop Deep Learning Techniques for Next Generation beyond Gigabit Ethernet Transceiver( II )
OpenAlex ID: W4402314153