Design of mid-infrared cascade micro-ring sensing devices using a machine learning algorithm

Jinghao Yang; Austin Caruso; Zhihai Lin; Junyan Li; Pao Tai Lin; Pao Tai Lin

doi:10.1364/JOSAB.431042

Journal of the Optical Society of America B
Vol. 38,
Issue 11,
pp. 3292-3300
(2021)
•https://doi.org/10.1364/JOSAB.431042

Design of mid-infrared cascade micro-ring sensing devices using a machine learning algorithm

Jinghao Yang, Austin Caruso, Zhihai Lin, Junyan Li, and Pao Tai Lin

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Jinghao Yang, Austin Caruso, Zhihai Lin, Junyan Li, and Pao Tai Lin, "Design of mid-infrared cascade micro-ring sensing devices using a machine learning algorithm," J. Opt. Soc. Am. B 38, 3292-3300 (2021)

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Abstract

An intelligent mid-infrared (mid-IR) integrated photonic device was demonstrated applying a machine learning (ML) algorithm. The design model and the estimation model of mid-IR micro-rings were trained by the artificial neural network (ANN) to create the performance-structure relationships. The sensing devices were then designed to align the micro-ring resonance with the characteristic mid-IR absorption wavelengths according to the gases of interest. Further applying the cascade micro-ring structures enables the device to monitor several gas analytes simultaneously. The ML-based mid-IR device provides a miniaturized sensing platform for remote and precise environmental monitoring.

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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Equations (14)

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Group	$N$	$W$ (µm)	$G$ (µm)	$T$ (µm)	$R$ (µm)
1	2.5	1	0.3	10	2.5
2	2.8	1	0.3	10	3.5
3	3.4	1.3	0.2	10	8
4	3.4	0.6	0.2	10	15
5	3.4	1	0.3	9	6.5

Group	Traditional Design	ML-Enabled Design
Accuracy	Very high	High
Efficiency	Low	High
Flexibility	Low	High

Group	$N$	$W$ (µm)	$G$ (µm)	$T$ (µm)	$R$ (µm)
1	2.5	1	0.3	10	2.5
2	2.8	1	0.3	10	3.5
3	3.4	1.3	0.2	10	8
4	3.4	0.6	0.2	10	15
5	3.4	1	0.3	9	6.5

Group	Traditional Design	ML-Enabled Design
Accuracy	Very high	High
Efficiency	Low	High
Flexibility	Low	High

Group	$N$	$W$ (µm)	$G$ (µm)	$T$ (µm)	$R$ (µm)
1	2.5	1	0.3	10	2.5
2	2.8	1	0.3	10	3.5
3	3.4	1.3	0.2	10	8
4	3.4	0.6	0.2	10	15
5	3.4	1	0.3	9	6.5

Group	$N$	$W$ (µm)	$G$ (µm)	$T$ (µm)	$R$ (µm)
1	2.5	1	0.3	10	2.5
2	2.8	1	0.3	10	3.5
3	3.4	1.3	0.2	10	8
4	3.4	0.6	0.2	10	15
5	3.4	1	0.3	9	6.5

Group	$N$	$W$ (µm)	$G$ (µm)	$T$ (µm)	$R$ (µm)
1	2.5	1	0.3	10	2.5
2	2.8	1	0.3	10	3.5
3	3.4	1.3	0.2	10	8
4	3.4	0.6	0.2	10	15
5	3.4	1	0.3	9	6.5

Group	$N$	$W$ (µm)	$G$ (µm)	$T$ (µm)	$R$ (µm)
1	2.5	1	0.3	10	2.5
2	2.8	1	0.3	10	3.5
3	3.4	1.3	0.2	10	8
4	3.4	0.6	0.2	10	15
5	3.4	1	0.3	9	6.5

Abstract

Data Availability

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Figures (18)

Tables (2)

Equations (14)

Journal of the Optical Society of America B

Group	$N$	$W$ (µm)	$G$ (µm)	$T$ (µm)	$R$ (µm)
1	2.5	1	0.3	10	2.5
2	2.8	1	0.3	10	3.5
3	3.4	1.3	0.2	10	8
4	3.4	0.6	0.2	10	15
5	3.4	1	0.3	9	6.5

Group	$N$	$W$ (µm)	$G$ (µm)	$T$ (µm)	$R$ (µm)
1	2.5	1	0.3	10	2.5
2	2.8	1	0.3	10	3.5
3	3.4	1.3	0.2	10	8
4	3.4	0.6	0.2	10	15
5	3.4	1	0.3	9	6.5