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Filter technologies implemented on CMOS image sensors for spectrally selective applications often use a combination of on-chip organic resists and an external substrate with multilayer dielectric coatings. The photopic-like and near-infrared bandpass filtering functions respectively required by ambient light sensing and user proximity detection through time-of-flight can be fully integrated on chip with multilayer metal-dielectric filters. Copper, silicon nitride, and silicon oxide are the materials selected for a technological proof-of-concept on functional wafers, due to their immediate availability in front-end semiconductor fabs. Filter optical designs are optimized with respect to specific performance criteria, and the robustness of the designs regarding process errors are evaluated for industrialization purposes.

It has been shown in literature that cross-shaped holes arrays can be made insensitive to polarization at normal incidence, and can even feature good stability for off-normal incidence. In this work we look for the optimal design rules to obtain high spectral stability conditions in the visible for those structures, through a complete review of all geometrical parameters using CMOS-compatible materials. Rigorous Coupled Wave Analysis (RCWA) simulations have been used to identify the most-impacting parameters and to determine typical ranges allowing for the realization of low-color errors image sensors whatever the light incidence. It appears that the two main parameters are the ratio of the arm width to the arm length of the crosses and the distance between crosses, which both have to be low to ensure stable responses of the filters. We demonstrate the results with CIE chromaticity diagrams reporting the responses of a RGB filter designed with the established rules under various illumination conditions.

Ge nanocrystals (Ge-NCs) embedded in SiN dielectrics with HfO2/SiO2 stack tunnel dielectrics were synthesized by utilizing low-energy (≤5 keV) ion implantation method followed by conventional thermal annealing at 800°C, the key variable being Ge+ ion implantation energy. Two different energies (3 and 5 keV) have been chosen for the evolution of Ge-NCs, which have been found to possess significant changes in structural and chemical properties of the Ge+-implanted dielectric films, and well reflected in the charge storage properties of the Al/SiN/Ge-NC + SiN/HfO2/SiO2/Si metal-insulator-semiconductor (MIS) memory structures. No Ge-NC was detected with a lower implantation energy of 3 keV at a dose of 1.5 × 1016 cm-2, whereas a well-defined 2D-array of nearly spherical and well-separated Ge-NCs within the SiN matrix was observed for the higher-energy-implanted (5 keV) sample for the same implanted dose. The MIS memory structures implanted with 5 keV exhibits better charge storage and retention characteristics compared to the low-energy-implanted sample, indicating that the charge storage is predominantly in Ge-NCs in the memory capacitor. A significant memory window of 3.95 V has been observed under the low operating voltage of ± 6 V with good retention properties, indicating the feasibility of these stack structures for low operating voltage, non-volatile memory devices.

We propose a new algorithm for estimating the location of an object in multichannel images when the noise is spatially disjointed from (nonoverlapping with) the target. This algorithm is optimal for nonoverlapping noise and for multichannel images in the maximum-likelihood sense. We consider the case in which the statistical parameters of the input scene are unknown and are estimated by observation. We assess the results for simulated images with white and Gaussian background, for a large scale of variances of the background noise, and different values of the contrast in the scene. We compare the results of this algorithm with the results obtained with two other algorithms, the optimal algorithm for monochannel nonoverlapping noise and the optimal algorithm for multichannel additive noise, and we show that in both cases improvement can be obtained. We show the efficiency of the estimation for real input scenes when the background noise is correlated clutter noise. This algorithm has the same complexity as correlation, and the improvement is obtained with no more calculation cost than with classic methods.