A rotational MEMS diffraction grating for realization of micro-sized spectroscope system

Yamamoto, Yuta; Shinozaki, R.; Oka, Yusaku; Asahi, Ippei; Ninomiya, Hideki; Shimokawa, Fusao; Oohira, Fumikazu; Takao, Hidekuni

doi:10.1109/transducers.2015.7180898

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…One possible way to improve the SNR is to increase the number of co-added scans to average the intra-spectral noise. [74,75] Till now, various spectrometers have been reported with the Czerny-Turner configuration for the visible range [76] and near-infrared range (NIR). [74,77,78] The resulted resolution of these reported NIR spectrometers was reported to be about 10 nm and the scanning speed was measured to be several milliseconds.…”

Section: Applications Of Mems Scanning Gratingsmentioning

confidence: 99%

MEMS gratings and their applications

Zhou

Lim

et al. 2021

International Journal of Optomechatronics

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Grating plays an essential role in various optical systems owing to its unique dispersion properties. In recent years, there is increasing demand to miniaturize optical systems for a wide range of field applications. Therefore, the integration of diffraction grating with MEMS technology provides an efficient way to build truly miniaturized optical systems. Till now, MEMS diffraction gratings have mainly been explored in two directions, namely MEMS scanning gratings and MEMS tunable gratings. MEMS scanning gratings are constructed with a variety of MEMS actuators to drive a grating platform to scan across the target, and they play a significant role in various scanning systems. Meanwhile, the dispersive properties of grating scanners make them attractive in wavelength sensing applications, including spectrometers and hyperspectral imaging systems. Tunable gratings typically employ MEMS actuators to dynamically change the diffraction properties, thus tuning its wavelength sensitivity for a specific application. Thus, this review will introduce these two types of MEMS gratings in detail and evaluate their efficiency and advantages in various fields.

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Section: Applications Of Mems Scanning Gratingsmentioning

confidence: 99%

MEMS gratings and their applications

Zhou

Lim

et al. 2021

International Journal of Optomechatronics

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“…It is capable of miniaturizing spectrometers and various MEMS based spectrometers have been developed [5], [6]. MEMS spectrometers can be classified into three categories: dispersive gratings [7]- [9], tunable filters [10]- [12], and Fourier transform infrared (FTIR) spectrometers [13]- [16]. MEMS-based FTIR, or μFTIR, has the advantages of higher signal to noise ratio (SNR) and spectral resolution as well as potentially low cost as only single IR photodetectors, instead of expensive IR detector arrays, are needed.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive On-Site Detection of Soybean Contents Based on An Electrothermal MEMS Fourier Transform Spectrometer

Wang

Liu

et al. 2019

IEEE Photonics J.

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A portable spectrophotometer has been developed to provide on-site detection of soybeans' moisture, protein, and fat contents in a nondestructive manner. The core module of this soybean analyzer is a micro Fourier transform infrared spectrometer (μFTIR) based on an electrothermal microelectromechanical systems (MEMSs) mirror. The electrothermal MEMS mirror is capable of generating a large piston motion of 320 μm at only 8.5 Vdc. The tilting of the MEMS mirror plate during its piston scanning affects the spectral repeatability of the μFTIR. In this paper, the effect of the MEMS mirror tilting on the spectral repeatability is analyzed and an automatic tilt realignment algorithm is developed to maintain high spectral repeatability, which is better than 0.01 in long term use. The μFTIR module has a footprint of 125 mm × 90 mm with a height of 50 mm. The whole instrument is designed for portability and on-site operation as well as the convenience of loading soybeans. More than 300 samples of soybeans are collected, 80% of which are used to establish a prediction model using chemometrics methods. The standard error is no more than 3% when using the prediction model to test the remaining 20% samples.

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