Abstract
This paper presents a BJT-based smart CMOS temperature sensor. The analog front-end circuit contains a bias circuit and a bipolar core; the data conversion interface features an incremental delta-sigma analog-to-digital converter. The circuit utilizes the chopping, correlated double sampling, and dynamic element matching techniques to mitigate the effects of process bias and nonideal device characteristics on measurement accuracy. Furthermore, based on the principle of charge conservation, the dynamic range utilization of the ADC increases. We propose a neural network that uses a multilayer convolutional perceptron to calibrate the sensor output results. Using the algorithm, the sensor achieves an inaccuracy of ±0.11 °C (3σ), exceeding the accuracy of ±0.23 °C (3σ) achieved without calibration. We implement the sensor in a 0.18 µm CMOS process, occupying an area of 0.42 mm2. It achieves a resolution of 0.01 °C and has a conversion time of 24 ms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.