Resolution of inverse problem by use of a Neural Network: Application to the characterization of optical grating

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Optical control of the homogeneity of a silicon grating

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Control of the homogeneity of an optical grating by a neural characterization

International audienceProgress in microelectronics has allowed the fabrication of optical gratings with small period-to-wavelength ratios, which are very useful in several applications, such as telecommunication and optical sensors. A rapid and nondestructive characterization process is essential to check the agreement of the produced with the expected structure. The main difficulty is in assuring sufficient homogeneity all along the grating surface. We show that neural characterization coupled with neural selection can be efficiently applied to this quality measurement. Depicted results concern a 1-µm-period grating fabricated by reactive ion etching on a silicon substrate

Caractérisation de composants optiques par des techniques d’intelligence artificielle

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Utilisation d'une ribre optique unimodale standard en capteur polarimétrique. Application à la détection de vibrations mécaniques

A theoretical and experimental study of the polarization properties change induced by a mechanical deformation of a standard single mode fiber is presented. These results have been used to design a vibration sensor by coiling a standard single mode fiber. Conditions for linear response and good sensitivity are discussed.Nous présentons une étude théorique et expérimentale des modifications de polarisation induites dans une fibre optique unimodale standard soumise à une perturbation mécanique. Ces résultats ont été utilisés pour réaliser un capteur de vibrations à l'aide d'une fibre optique bobinée. Nous discutons les conditions pour obtenir une réponse linéaire et une bonne sensibilité

Neural selection of the optimal optical signature for a rapid characterization of a submicrometer period grating

The characterization of gratings with small period-to-wavelength ratios can be achieved by solving the inverse problem of the diffraction. The use of a neural network has shown several advantages: it is a non-destructive, non-local and non-invasive method. However, although the calculation of results is instantaneous, the neural characterizations already published require the measurement of many diffracted intensities and can so need a long measurement time. We present, in this paper, a neural selection process called heuristic variable selection. This method reduces the number of diffractive efficiencies allowing a correct reconstruction of the profile shape according to an expected accuracy. In the same way, the non-redundancy of the data composing the optical signature is ensured. We relate a 1-�m period grating etched in silicon which could be characterized with only six measurements when a trapezoidal profile shape is assumed

Fabrication of buried corrugated waveguides by wafer direct bonding

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