Diffraction gratings are among the most commonly used optical elements in applications ranging from spectroscopy and metrology to lasers. Numerous methods have been adopted for the fabrication of gratings, including microelectromechanical system (MEMS) fabrication which is by now mature and presents opportunities for tunable gratings through inclusion of an actuation mechanism. We have designed, modeled, fabricated and tested a silicon based pitch tunable diffraction grating (PTG) with relatively large resolving power that could be deployed in a spaceborne imaging spectrometer, for example in a picosatellite. We have carried out a detailed analytical modeling of PTG, based on a mass spring system. The device has an effective fill factor of 52% and resolving power of 84. Tuning provided by electrostatic actuation results in a displacement of 2.7 sans-serifμnormalm at 40 V. Further, we have carried out vibration testing of the fabricated structure to evaluate its feasibility for spaceborne instruments.

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Integral field spectroscopy (IFS) is a well-established method for measuring spectral intensity data of the form s(x,y,λ), where x, y are spatial coordinates and λ is the wavelength. In most flavors of IFS, there is a trade-off between sampling (x,y) and the measured wavelength band Δλ. Here we present the first, to our knowledge, attempt to overcome this trade-off by use of computational imaging and measurement diversity. We implement diversity by including a grating in our design, which allows rotation of the dispersed spectra between measurements. The raw intensity data captured from the rotated grating positions are then processed by an inverse algorithm that utilizes sparsity in the data. We present simulated results from spatial-spectral data in the experimental dataset. We used non-overlapping portions of the dataset to train our sparsity priors in the form of the dictionary, and to test the reconstruction quality. We found that, depending on the level of noise in the measurement, diversity up to a maximum number of measurements is beneficial in terms of reducing error, and yields diminishing returns if even more measurements are taken.

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