Review on integrated optical sensors and its applications

Correa-Mena, A. G.; González, L.; Quintero-Rodriguez, L. J.; Zaldívar-Huerta, I. E.

doi:10.1109/mhtc.2017.7926204

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…Hence, a cheap, simple, fast, and accurate detection method is highly needed [7][8][9]. On-chip refractive index (RI) optical sensors are very promising to fulfill these requirements, as they allow for fast, label-free detection with high sensitivity and immunity to electromagnetic interference [10][11][12][13][14]. Moreover, when based on complementary metal-oxide-semiconductor (CMOS) compatible technologies, such as silicon-on-insulator (SOI), they offer low-cost and mass-scale fabrication.…”

Section: Introductionmentioning

confidence: 99%

On-Chip Sensing System Employing Wavelength Splitting for Noise Suppression

Shamy,

Swillam,

2024

Preprint

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In this work, we present a novel refractive index (RI) sensing system that is capable of suppressing optical phase errors (noise). Phase errors, for instance, due to process and temperature variations, limit the detection accuracy and the limit of detection (LoD) of the sensor. The proposed system uses four loop-terminated Mach-Zehnder Interferometers (LT-MZI) to achieve wavelength splitting. LT-MZI allows us to tune the output spectrum using its directional couplers coefficients. Wavelength splitting occurs with accordance to RI change using two LT-MZIs with opposite wavelength sensitivity. By determining two independent parameters, namely the wavelength splitting and the average wavelength, the system is capable to differentiate between phase changes due to medium index change and phase changes due to any other effects (noise), which maximizes the detection accuracy. For interferometers with the same waveguide structure in both of the arms, this system can totally eliminate any optical phase errors. This wavelength splitting cannot be achieved using the conventional MZI. Another two LT-MZIs with a quarter of the length are used to double the detection range. This sensing system can be used for various chemical and biological detections using any platform and operating wavelength. Using this system, a liquid sensor based on the widespread CMOS compatible silicon-on-insulator (SOI) technology and operating in the near-infrared is designed. While the SOI platform can achieve high sensitivity to medium index change and compact devices due to the high index contrast, it is also very sensitive to optical phase errors. However, our proposed system can eliminate these errors. Finite difference eigenmode (FDE) and finite difference time domain (FDTD) solvers are used to design and optimize the sensor’s performance. The sensor achieves a figure of merit (FOM) of 1233 RIU-1, corresponding to an intrinsic LoD of 8e-4, and a sensitivity as high as 7890 nm/RIU with a sensing arm length of only 500 µm, which are 3 and 2 times higher than single MZI, respectively. Finally, this sensor has a much higher detection range, 6.3 times higher than a single MZI and is able to suppress optical noise.

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Section: Introductionmentioning

confidence: 99%

On-Chip Sensing System Employing Wavelength Splitting for Noise Suppression

Shamy,

Swillam,

2024

Preprint

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“…5 Integrated optical sensing falls into two categories, either detecting the real component n of the medium in refractive index sensing, or determining the imaginary component k through absorption sensing. 6 Refractive index detectors are not selective without additional structures, while absorption is dependant on the volume of the substance, as longer interaction lengths between the light and sample will result in higher absorption and therefore higher sensitivity. 7 One way to circumvent this is the use of high quality factor micro-cavity, such as a MRR, where the light circulates multiple times through the ring hence having a much higher "effective" interaction length, increasing it up from the micrometer range to one in the millimeters.…”

Section: Introductionmentioning

confidence: 99%

Prediction of medium chemical concentration with micro-ring resonators and deep learning

Mikhail

Shamy

Swillam

et al. 2023

Smart Photonic and Optoelectronic Integrated Circuits 2023

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A new approach for determining the concentration composition of a multi-element media using a micro-ring resonator (MRR) is proposed which allows for noise removal as well as moderately higher average accuracy. This method uses two neural networks, namely a convolutional neural network (CNN) and a deep neural network (DNN). The CNN differentiates the transmission spectrum from the noise. This spectrum is used to obtain selected features before being fed into the DNN, which determines the concentration of each chemical in the analyte. Both models are trained to work with a silicon on-insulator ring resonator operating between the infrared wavelengths of λ=1.46 µm to λ=1.6µm on mixtures of water,ethanol,methanol, and propanol by using simulation data obtained from finite difference eigenmode, although the same approach can be used with other designs and chemical combinations. The CNN was trained using the MRR transmission spectra superimposed with white Gaussian noise as well as Poisson noise to mimic various noise sources, while the DNN underwent training on the extracted features. Average Root-Mean-Square Error was for a range of concentrations from 0.0357-75% is 5.531%.

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“…Furthermore, they can provide real time monitoring capabilities, label free detection, limited sample consumption and resistance to electromagnetic interference. Integrated optical sensing falls into two categories, either detecting the real component n of the medium in refractive index sensing, or determining the imaginary component k through absorption sensing [6]. Refractive index sensors may use optical devices such as Mach-Zehnder interferometers, microring resonators (MRR), or surface plasmon polariton to detect n. They are especially useful in applications where the volume of samples is small, as n does not depend on the volume [6].…”

Section: Introductionmentioning

confidence: 99%

“…Integrated optical sensing falls into two categories, either detecting the real component n of the medium in refractive index sensing, or determining the imaginary component k through absorption sensing [6]. Refractive index sensors may use optical devices such as Mach-Zehnder interferometers, microring resonators (MRR), or surface plasmon polariton to detect n. They are especially useful in applications where the volume of samples is small, as n does not depend on the volume [6]. However, they are not selective without additional structures, meaning that substances with the same detected n at a given wavelength cannot be distinguished.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Performance Of On-Chip Integrated Biosensor Using Deep Learning

Mikhail,

Shamy,

Swillam

et al. 2023

Preprint

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A new approach for determining the concentration composition of a multi-element media using a micro-ring resonator (MRR) is proposed which allows for both electrical and thermal noise removal as well as moderately higher average accuracy. This method uses two neural networks, namely a convolutional neural network (CNN) and a deep neural network (DNN). The CNN differentiates the transmission spectrum from the noise. This spectrum is used to obtain selected features before being fed into the DNN, which determines the concentration of each chemical in the analyte. Both models are trained to work using simulated data from a silicon on-insulator ring resonator operating between the infrared wavelengths of λ=1.46 µm to λ=1.6 μm on mixtures of water, ethanol, methanol, and propanol, although the same approach can be used with other designs and substances. The CNN was trained using the MRR transmission spectra superimposed with white Gaussian noise as well as Poisson noise to mimic different noise sources, while the DNN underwent training on the extracted features. Average root-mean-square error for element concentration for the entire system is 0.083 % for a range of concentrations from 0.0357-0.75 %, and the largest error had a value of 0.68% concentration.

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Review on integrated optical sensors and its applications

Cited by 10 publications

References 17 publications

On-Chip Sensing System Employing Wavelength Splitting for Noise Suppression

On-Chip Sensing System Employing Wavelength Splitting for Noise Suppression

Prediction of medium chemical concentration with micro-ring resonators and deep learning

Enhanced Performance Of On-Chip Integrated Biosensor Using Deep Learning

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