ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation

Wei Chien Kuo; Li Wei Liu; Yen Chin Liao; Hsie Chia Chang

doi:10.1109/SOCC46988.2019.1570561880

ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation

Wei Chien Kuo, Li Wei Liu, Yen Chin Liao, Hsie Chia Chang

Institute of Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a machine-learning-based post signal processing to calibrate thermal sensors. The proposed calibration scheme is shown to be immune to the interference from the environment and fulfills the high-resolution requirements of human body temperature measurements. The sensing module comprises two resistive sensing circuits, one is for sensing the external temperature, and the other is for sensing the internal die temperature. By using these two thermal outputs, we trained two-dimensional multivariate Gaussian models for several temperature intervals. Higher accuracy can be obtained via the probability-based estimation. The simulation results show high accuracy even in a noisy environment. The proposed algorithm is implemented and fabricated in UMC 0.18m CMOS-MEMS technology. The sensor chip is tested by an embedded system (ARM V2M-MPS2). The measurement results show that the proposed method can effectively improve the accuracy from 1 degree Celsius to 0.1 degree Celsius.

Original language	English
Title of host publication	Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019
Editors	Danella Zhao, Arindam Basu, Magdy Bayoumi, Gwee Bah Hwee, Ge Tong, Ramalingam Sridhar
Publisher	IEEE Computer Society
Pages	113-117
Number of pages	5
ISBN (Electronic)	9781728134826
DOIs	https://doi.org/10.1109/SOCC46988.2019.1570561880
State	Published - Sep 2019
Event	32nd IEEE International System on Chip Conference, SOCC 2019 - Singapore, Singapore Duration: 3 Sep 2019 → 6 Sep 2019

Publication series

Name	International System on Chip Conference
Volume	2019-September
ISSN (Print)	2164-1676
ISSN (Electronic)	2164-1706

Conference

Conference	32nd IEEE International System on Chip Conference, SOCC 2019
Country/Territory	Singapore
City	Singapore
Period	3/09/19 → 6/09/19

Keywords

Intelligent IoT
Machine Learning

Access to Document

10.1109/SOCC46988.2019.1570561880

Cite this

Kuo, W. C., Liu, L. W., Liao, Y. C., & Chang, H. C. (2019). ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation. In D. Zhao, A. Basu, M. Bayoumi, G. B. Hwee, G. Tong, & R. Sridhar (Eds.), Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019 (pp. 113-117). [9088089] (International System on Chip Conference; Vol. 2019-September). IEEE Computer Society. https://doi.org/10.1109/SOCC46988.2019.1570561880

Kuo, Wei Chien ; Liu, Li Wei ; Liao, Yen Chin et al. / ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation. Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019. editor / Danella Zhao ; Arindam Basu ; Magdy Bayoumi ; Gwee Bah Hwee ; Ge Tong ; Ramalingam Sridhar. IEEE Computer Society, 2019. pp. 113-117 (International System on Chip Conference).

@inproceedings{f42a7624936346d682f7af5f0754221b,

title = "ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation",

abstract = "This paper presents a machine-learning-based post signal processing to calibrate thermal sensors. The proposed calibration scheme is shown to be immune to the interference from the environment and fulfills the high-resolution requirements of human body temperature measurements. The sensing module comprises two resistive sensing circuits, one is for sensing the external temperature, and the other is for sensing the internal die temperature. By using these two thermal outputs, we trained two-dimensional multivariate Gaussian models for several temperature intervals. Higher accuracy can be obtained via the probability-based estimation. The simulation results show high accuracy even in a noisy environment. The proposed algorithm is implemented and fabricated in UMC 0.18m CMOS-MEMS technology. The sensor chip is tested by an embedded system (ARM V2M-MPS2). The measurement results show that the proposed method can effectively improve the accuracy from 1 degree Celsius to 0.1 degree Celsius.",

keywords = "Intelligent IoT, Machine Learning",

author = "Kuo, {Wei Chien} and Liu, {Li Wei} and Liao, {Yen Chin} and Chang, {Hsie Chia}",

year = "2019",

month = sep,

doi = "10.1109/SOCC46988.2019.1570561880",

language = "English",

series = "International System on Chip Conference",

publisher = "IEEE Computer Society",

pages = "113--117",

editor = "Danella Zhao and Arindam Basu and Magdy Bayoumi and Hwee, {Gwee Bah} and Ge Tong and Ramalingam Sridhar",

booktitle = "Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019",

address = "United States",

note = "32nd IEEE International System on Chip Conference, SOCC 2019 ; Conference date: 03-09-2019 Through 06-09-2019",

}

Kuo, WC, Liu, LW, Liao, YC & Chang, HC 2019, ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation. in D Zhao, A Basu, M Bayoumi, GB Hwee, G Tong & R Sridhar (eds), Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019., 9088089, International System on Chip Conference, vol. 2019-September, IEEE Computer Society, pp. 113-117, 32nd IEEE International System on Chip Conference, SOCC 2019, Singapore, Singapore, 3/09/19. https://doi.org/10.1109/SOCC46988.2019.1570561880

ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation. / Kuo, Wei Chien; Liu, Li Wei; Liao, Yen Chin et al.
Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019. ed. / Danella Zhao; Arindam Basu; Magdy Bayoumi; Gwee Bah Hwee; Ge Tong; Ramalingam Sridhar. IEEE Computer Society, 2019. p. 113-117 9088089 (International System on Chip Conference; Vol. 2019-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation

AU - Kuo, Wei Chien

AU - Liu, Li Wei

AU - Liao, Yen Chin

AU - Chang, Hsie Chia

PY - 2019/9

Y1 - 2019/9

N2 - This paper presents a machine-learning-based post signal processing to calibrate thermal sensors. The proposed calibration scheme is shown to be immune to the interference from the environment and fulfills the high-resolution requirements of human body temperature measurements. The sensing module comprises two resistive sensing circuits, one is for sensing the external temperature, and the other is for sensing the internal die temperature. By using these two thermal outputs, we trained two-dimensional multivariate Gaussian models for several temperature intervals. Higher accuracy can be obtained via the probability-based estimation. The simulation results show high accuracy even in a noisy environment. The proposed algorithm is implemented and fabricated in UMC 0.18m CMOS-MEMS technology. The sensor chip is tested by an embedded system (ARM V2M-MPS2). The measurement results show that the proposed method can effectively improve the accuracy from 1 degree Celsius to 0.1 degree Celsius.

AB - This paper presents a machine-learning-based post signal processing to calibrate thermal sensors. The proposed calibration scheme is shown to be immune to the interference from the environment and fulfills the high-resolution requirements of human body temperature measurements. The sensing module comprises two resistive sensing circuits, one is for sensing the external temperature, and the other is for sensing the internal die temperature. By using these two thermal outputs, we trained two-dimensional multivariate Gaussian models for several temperature intervals. Higher accuracy can be obtained via the probability-based estimation. The simulation results show high accuracy even in a noisy environment. The proposed algorithm is implemented and fabricated in UMC 0.18m CMOS-MEMS technology. The sensor chip is tested by an embedded system (ARM V2M-MPS2). The measurement results show that the proposed method can effectively improve the accuracy from 1 degree Celsius to 0.1 degree Celsius.

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KW - Machine Learning

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U2 - 10.1109/SOCC46988.2019.1570561880

DO - 10.1109/SOCC46988.2019.1570561880

M3 - Conference contribution

AN - SCOPUS:85085217705

T3 - International System on Chip Conference

SP - 113

EP - 117

BT - Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019

A2 - Zhao, Danella

A2 - Basu, Arindam

A2 - Bayoumi, Magdy

A2 - Hwee, Gwee Bah

A2 - Tong, Ge

A2 - Sridhar, Ramalingam

PB - IEEE Computer Society

T2 - 32nd IEEE International System on Chip Conference, SOCC 2019

Y2 - 3 September 2019 through 6 September 2019

ER -

Kuo WC, Liu LW, Liao YC, Chang HC. ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation. In Zhao D, Basu A, Bayoumi M, Hwee GB, Tong G, Sridhar R, editors, Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019. IEEE Computer Society. 2019. p. 113-117. 9088089. (International System on Chip Conference). doi: 10.1109/SOCC46988.2019.1570561880

ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation

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