An energy-efficient truly all-digital temperature sensor for SoC applications

Author(s):  
Tzu-Yuan Kuo ◽  
Keng-Jui Chang ◽  
Jen-Hsiang Lee ◽  
Zong-Wu He ◽  
Jinn-Shyan Wang
Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1700
Author(s):  
Anca Mihaela Vasile (Dragan) ◽  
Alina Negut ◽  
Adrian Tache ◽  
Gheorghe Brezeanu

An EEPROM (electrically erasable programmable read-only memory) reprogrammable fuse for trimming a digital temperature sensor is designed in a 0.18-µm CMOS EEPROM. The fuse uses EEPROM memory cells, which allow multiple programming cycles by modifying the stored data on the digital trim codes applied to the thermal sensor. By reprogramming the fuse, the temperature sensor can be adjusted with an increased trim variation in order to achieve higher accuracy. Experimental results for the trimmed digital sensor showed a +1.5/−1.0 ℃ inaccuracy in the temperature range of −20 to 125 ℃ for 25 trimmed DTS samples at 1.8 V by one-point calibration. Furthermore, an average mean of 0.40 ℃ and a standard deviation of 0.70 ℃ temperature error were obtained in the same temperature range for power supply voltages from 1.7 to 1.9 V. Thus, the digital sensor exhibits similar performances for the entire power supply range of 1.7 to 3.6 V.


Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1291
Author(s):  
Giuseppe Schirripa Schirripa Spagnolo ◽  
Fabio Leccese

Nowadays, signal lights are made using light-emitting diode arrays (LEDs). These devices are extremely energy efficient and have a very long lifetime. Unfortunately, especially for yellow/amber LEDs, the intensity of the light is closely related to the junction temperature. This makes it difficult to design signal lights to be used in naval, road, railway, and aeronautical sectors, capable of fully respecting national and international regulations. Furthermore, the limitations prescribed by the standards must be respected in a wide range of temperature variations. In other words, in the signaling apparatuses, a system that varies the light intensity emitted according to the operating temperature is useful/necessary. In this paper, we propose a simple and effective solution. In order to adjust the intensity of the light emitted by the LEDs, we use an LED identical to those used to emit light as a temperature sensor. The proposed system was created and tested in the laboratory. As the same device as the ones to be controlled is used as the temperature sensor, the system is very stable and easy to set up.


2010 ◽  
Vol 29-32 ◽  
pp. 349-353
Author(s):  
Jing Tang ◽  
En Xing Zheng

The paper designs a temperature control system based on AT89C51 and DS18B20. The design uses the DS18B20 digital temperature sensor as the temperature acquisition unit and the AT89C51 microcontroller unit to control them, not only have the advantages that easy to control and with good flexibility, but also can greatly enhance the controlled temperature index.


2015 ◽  
Vol 23 (8) ◽  
pp. 1508-1517 ◽  
Author(s):  
Young-Jae An ◽  
Dong-Hoon Jung ◽  
Kyungho Ryu ◽  
Seung-Han Woo ◽  
Seong-Ook Jung

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6389
Author(s):  
Kyriakos Koritsoglou ◽  
Vasileios Christou ◽  
Georgios Ntritsos ◽  
Georgios Tsoumanis ◽  
Markos G. Tsipouras ◽  
...  

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).


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