A low-cost mobile device for skin tone measurement using filter array spectrum sensor

Chang, Cheng-Chun; Chuang, Yung-Chi; Wu, Chien-Ta; Choi, Byung Il; Lee, Kwansik; Woo, Seongsu; Rao, S. V. Subba; Kim, Jihoon

doi:10.1109/icsens.2014.6985044

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Therefore, the output signals must be processed so as to reconstruct spectral characteristics of incident light. In order to solve the inverse problem of determining input signals (optical spectrum of incident light) from output signals, we calculate spectral characteristics using Tikhonov regularization .…”

Section: Design and Numerical Analysismentioning

confidence: 99%

“…In the recent years, there are reports about fabrication of spectrometers that combine plasmonic color filters and CMOS (Complementary Metal-Oxide Semiconductor) sensors [11][12][13][14][15][16]. In these devices, color filters are formed on each pixel of CMOS sensor, and spectral information is obtained through calculation of signals from each pixel.…”

Section: Introductionmentioning

confidence: 99%

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Plasmonic Color Filters Integrated on a Photodiode Array

Ema

Kanamori

Sai

et al. 2017

Elect Comm in Japan

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SUMMARY Arrayed plasmonic color filters integrated on Si photodiodes with 21 elements were experimentally demonstrated. The plasmonic color filters consisted of nanodot subwavelength periodic structures. Optical characteristics of the color filters were measured and compared with calculated results. In order to obtain spectrum of an incident light from the signal of photodiodes, spectrum reconstruction using Tikhonov regularization was adapted. In the spectral sensitivity of each photodiode, the peak wavelength was adjusted between 400 and 850 nm in wavelength by the period of nanodot structure on each photodiode. The measured peak wavelengths roughly agreed with calculated values with errors less than 30 nm in wavelength. Spectral characteristics of a monochromatic light were measured by fabricated device and spectrum reconstruction was carried out. The position of the center wavelength could be successfully detected.

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Section: Design and Numerical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plasmonic Color Filters Integrated on a Photodiode Array

Ema

Kanamori

Sai

et al. 2017

Elect Comm in Japan

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“…The key hardware components generally consist of a set of spectral encoders with distinct broadband transmission spectra and the detector beneath, where the spectral encoders sample the unknown spectrum and the intensities collected by the detector serve as the input of the reconstruction network. Theoretically, optical elements which can exhibit diverse spectral responses, are appropriate candidates for spectral encoders, such as quantum dots, − nanowires, , thin films, − photonic crystal slabs, − and metasurfaces. − The distinct optical properties of each spectral encoder are precalibrated. Afterward, the spectrum under measured is recovered using the reconstruction network.…”

mentioning

confidence: 99%

Deep Learning-Based Miniaturized All-Dielectric Ultracompact Film Spectrometer

Wen

Gao

et al. 2022

ACS Photonics

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Conventional benchtop spectrometers with bulky dispersive optics and long optical path lengths display limitations where the significance of miniaturization, real-time detection, and low cost transcend the ultrafine resolution and wide spectral range. Here, we demonstrate a miniaturized all-dielectric ultracompact film spectrometer based on deep learning working in the single-shot mode. The scheme employs 16 spectral encoders with simple five-layer film stacks where merely the thickness of the intermediate high-index modulation layer is varied to realize unique encoded transmission spectra. Structural parameters as well as transmission spectra of the filters are predesigned to guarantee weak correlation and highly efficient encoding. Leveraging a trained reconstruction network, the absolute spectra of various nonluminous samples are successfully reconstructed excluding the emitting spectrum of the light source and the spectral response of the detector. The remarkable reconstructed spectral imaging result for the color board is presented and the reconstructed spectra match well with the measured ones for different patches using the identical network. We utilized the least number of spectral encoders ever since to guarantee efficient encoding, along with the single thickness-variant modulation layer, which shows potential for mass, rapid, large-area production by combining deposition with nanoimprint. Instead of the synthetic Gaussian line shape spectra, a training dataset composed of diverse spectrum types is adopted to achieve fine generalization of the trained reconstruction network. In addition, by retraining the neural network, the reconstruction network is modified to fit for the actual filter functions of the spectral encoders, thus better reconstruction performance. The proposed miniaturized spectrometer has great prospects in the fields of consumer electronics, environmental monitoring, and disaster prevention.

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