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.
To evaluate electrical characteristics of the 1T charge-modulation pixel, we propose two design configurations: one is a
2.2μm-pitch, rectangular-gate pixel, and the other is a 1.4μm-pitch, ring-gate pixel. The former allows the transistor size
to be minimized, but requires surrounding STI (Shallow Trench Isolation) to reduce electrical crosstalk. The latter is
advantageous in terms of pixel size and fill factor, mainly thanks to STI suppression. The two design configurations are respectively integrated in test chips. Our measured results confirm the scaling law: reducing pixel size improves conversion gain, but degrades full well capacity (FWC). They also show that dark current of the 1.4μm-pitch ring-gate pixel is much lower than the 2.2μm-pitch rectangular-gate counterpart. This low dark current achievement may be explained by: i) suppression of STI-induced surface leakage current component, ii) smooth-shape layout to minimize band-to-band tunneling effect, and iii) smaller pixel size with smaller depletion areas which has, accordingly, lower thermally-generated dark current components. The 1.4μm-pitch ring-gate pixel also has lower noise, especially much lower dark FPN. This seems to confirm that dark FPN may have a large contribution from dark current generation. The dynamic range for the 1.4μm-pitch pixel is larger, meaning that signal-to-noise ratio outweighs FWC degradation. However, the sensitivity, like FWC, is also degraded in the same proportion. There are possibilities of improvements especially by process optimization.
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