Machine learning to predict junction temperature based on optical characteristics in solid-state lighting devices: A test on WLEDs

Name: Machine learning to predict junction temperature based on optical characteristics in solid-state lighting devices: A test on WLEDs
Author: Azarifar, Mohammad, Ocaksönmez, Kerem, Cengiz, Ceren, Aydoğan, Reyhan, Arık, Mehmet

İsim	Machine learning to predict junction temperature based on optical characteristics in solid-state lighting devices: A test on WLEDs
Yazar	Azarifar, Mohammad, Ocaksönmez, Kerem, Cengiz, Ceren, Aydoğan, Reyhan, Arık, Mehmet
Basım Tarihi:	2022-08
Basım Yeri	- MDPI
Konu	Gradient boosted trees, Junction temperature, Light emitting diodes, Machine learning, Random forest, Solid-state lighting, Emperature prediction
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2072-666X
Kayıt Numarası	e93b0c73-e3ad-4abc-ba89-d5515691833f
Lokasyon	Computer Science, Mechanical Engineering
Tarih	2022-08
Notlar	EVATEG Center ; Ozyegin University
Örnek Metin	While junction temperature control is an indispensable part of having reliable solid-state lighting, there is no direct method to measure its quantity. Among various methods, temperature-sensitive optical parameter-based junction temperature measurement techniques have been used in practice. Researchers calibrate different spectral power distribution behaviors to a specific temperature and then use that to predict the junction temperature. White light in white LEDs is composed of blue chip emission and down-converted emission from photoluminescent particles, each with its own behavior at different temperatures. These two emissions can be combined in an unlimited number of ways to produce diverse white colors at different brightness levels. The shape of the spectral power distribution can, in essence, be compressed into a correlated color temperature (CCT). The intensity level of the spectral power distribution can be inferred from the luminous flux as it is the special weighted integration of the spectral power distribution. This paper demonstrates that knowing the color characteristics and power level provide enough information for possible regressor trainings to predict any white LED junction temperature. A database from manufacturer datasheets is utilized to develop four machine learning-based models, viz., k-Nearest Neighbor (KNN), Radius Near Neighbors (RNN), Random Forest (RF), and Extreme Gradient Booster (XGB). The models were used to predict the junction temperatures from a set of dynamic opto-thermal measurements. This study shows that machine learning algorithms can be employed as reliable novel prediction tools for junction temperature estimation, particularly where measuring equipment limitations exist, as in wafer-level probing or phosphor-coated chips.
DOI	10.3390/mi13081245
Cilt	13

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Machine learning to predict junction temperature based on optical characteristics in solid-state lighting devices: A test on WLEDs

Yazar Azarifar, Mohammad, Ocaksönmez, Kerem, Cengiz, Ceren, Aydoğan, Reyhan, Arık, Mehmet

Basım Tarihi 2022-08

Basım Yeri - MDPI

Konu Gradient boosted trees, Junction temperature, Light emitting diodes, Machine learning, Random forest, Solid-state lighting, Emperature prediction

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2072-666X

Kayıt Numarası e93b0c73-e3ad-4abc-ba89-d5515691833f

Lokasyon Computer Science, Mechanical Engineering

Tarih 2022-08

Notlar EVATEG Center ; Ozyegin University

Örnek Metin While junction temperature control is an indispensable part of having reliable solid-state lighting, there is no direct method to measure its quantity. Among various methods, temperature-sensitive optical parameter-based junction temperature measurement techniques have been used in practice. Researchers calibrate different spectral power distribution behaviors to a specific temperature and then use that to predict the junction temperature. White light in white LEDs is composed of blue chip emission and down-converted emission from photoluminescent particles, each with its own behavior at different temperatures. These two emissions can be combined in an unlimited number of ways to produce diverse white colors at different brightness levels. The shape of the spectral power distribution can, in essence, be compressed into a correlated color temperature (CCT). The intensity level of the spectral power distribution can be inferred from the luminous flux as it is the special weighted integration of the spectral power distribution. This paper demonstrates that knowing the color characteristics and power level provide enough information for possible regressor trainings to predict any white LED junction temperature. A database from manufacturer datasheets is utilized to develop four machine learning-based models, viz., k-Nearest Neighbor (KNN), Radius Near Neighbors (RNN), Random Forest (RF), and Extreme Gradient Booster (XGB). The models were used to predict the junction temperatures from a set of dynamic opto-thermal measurements. This study shows that machine learning algorithms can be employed as reliable novel prediction tools for junction temperature estimation, particularly where measuring equipment limitations exist, as in wafer-level probing or phosphor-coated chips.

DOI 10.3390/mi13081245

Cilt 13