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A CMOS Temperature Sensor with a Smart Calibrated Inaccuracy of ±0.11 (3σ).
Wei, Rongshan; Lin, Huishan; Chen, Qunchao; Huang, Gongxing; Hu, Wei.
Afiliación
  • Wei R; College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
  • Lin H; College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
  • Chen Q; College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
  • Huang G; College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
  • Hu W; College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
Sensors (Basel) ; 23(11)2023 May 27.
Article en En | MEDLINE | ID: mdl-37299859
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Redes Neurales de la Computación Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Redes Neurales de la Computación Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China