Silicon-based Complementary Metal Oxide Semiconductor (CMOS) image sensors are widely used for visible range detection systems. However, when it comes to near-infrared-range (NIR) applications like face recognition or Augmented/Virtual Reality (AR/VR), these sensors are much less efficient. This is due to the poor absorption of Silicon at such wavelengths. A well-known solution studied in-depth over the past few years to address this issue consists of etching diffractive structures into the Silicon. The incoming light is therefore diffracted inside the photodiode, increasing the optical path, and thus improving the Quantum Efficiency (QE) of the pixel. However, the Modulation Transfer Function (MTF) of the sensor is degraded in return on account of the increased light flux crossing from one pixel to the other, being eventually absorbed in the wrong pixel, an optical crosstalk that ends up degrading the MTF. Here, using Finite Difference Time Domain (FDTD) simulations of precisely the same Slanted Edge method as used in MTF characterization, we positively evaluate a new methodology to simulate the MTF of the sensor. We compared simulated results with characterization ones on actual pixels in several distinct configurations. We studied sensors’ MTF without any diffractive structures and others with various structures designed to influence the MTF more specifically in one direction (horizontal or vertical) at 940 nm. We demonstrated good agreements between simulations and characterizations, showing highly correlated tendencies across the whole studied set and giving us parameter predictive power on the MTF for future innovative pixel designs.
Next-generation BSI CMOS Imager Sensors are strongly driven by novel applications in depth sensing, mainly operating in the NIR (940nm) spectrum. As a result, the need for higher pixel sensitivity while shrinking pixel pitch is more present than ever. In this work, we present a new technology platform based on ad-hoc nano diffractor geometries, integrated in the Back Side of BSI CIS that allow to drastically improve the QE of the sensor for pitches varying from 10 μm down to 2.2 μm, co-optimized for both optical and electronic pixel performance.
Due to their low-cost fabrication process and high efficiency, silicon-based Complementary Metal Oxide Semiconductor (CMOS) image sensors are the reference in term of detection in the visible range. However, their optical performances are toughly degraded in the Near Infrared (NIR). For such wavelengths, Silicon has a small absorption coefficient, leading to a very poor Quantum Efficiency (QE). A solution to improve it is to implement structures like pyramids that are etched in the Silicon layer. This will lead to diffraction inside the photodiode, enhancing the light path and therefore the absorption. Using Finite Difference Time Domain (FDTD) simulations, we demonstrated a huge QE enhancement at 940nm on real pixels, by implementing this kind of diffractive structures and we finally confirmed these results by characterizations. We obtained QE values up to 47% at 940nm for our 3.2μm pixel, corresponding to a gain of 2 comparing to a pixel without any diffractive structures. We also measured the Modulation Transfer Function (MTF), to evaluate how this figure of merit is impacted by the addition of these structures. As expected, the MTF was degraded when we added these diffractive patterns but were still high looking at the values. We indeed demonstrated MTF values going up to 0.55 at Nyquist/2 frequency and 0.35 at Nyquist frequency. Looking not only at QE values but also at MTF ones, these are very promising results that could be used in many different NIR applications like face recognition, Light Detection and Ranging (LIDAR) or AR/VR.
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