Photon management with superlattice for image sensor pixels
Source abstract
Abstract
It is important to increase light absorption and quantum efficiency in image sensor pixels, especially for wavelength ranges in which material absorption is weak. Surface textures, including nanostructure lattices, have been invented for significant improvement in light absorption. Those nanostructures typically support a number of physical processes for which the optimal geometries are different. We design a class of nanostructure superlattices to enable the co-optimization of different physical processes for further enhancement in light absorption.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Thin-Film Transistor Technologies · Transition Metal Oxide Nanomaterials · CCD and CMOS Imaging Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Physics, Huazhong University of Science and Technology 1 , Wuhan 430074, China
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology 2 , Wuhan 430074, China
References (29)
Show source-provided references
1. Pixel technology for improving IR quantum efficiency of backside-illuminated CMOS image sensor
2. Nanophotonics contributions to state-of-the-art CMOS image sensors
3. CMOS-based bio-image sensor spatially resolves neural activity-dependent proton dynamics in the living brain10.1038/s41467-020-14571-y ↗
4. Ir sensitivity enhancement of CMOS image sensor with diffractive light trapping pixels10.1038/s41598-017-04200-y ↗
5. The state-of-the-art of mainstream CMOS image sensors
6. Review application of nanostructured black silicon10.1186/s11671-018-2523-4 ↗
7. Nano-fabrication methods and novel applications of black silicon10.1016/j.sna.2019.04.044 ↗
8. Ultrafast laser-induced black silicon, from micro-nanostructuring, infrared absorption mechanism, to high performance detecting devices10.1016/j.mtnano.2020.100078 ↗
9. Light management for photovoltaics using high-index nanostructures10.1038/nmat3921 ↗
10. 2015
11. Light trapping in photonic structures
12. Photovoltatronics: Intelligent PV-based devices for energy and information applications10.1039/d0ee02491k ↗
13. Absorption enhancement in ultrathin crystalline silicon solar cells with antireflection and light-trapping nanocone gratings10.1021/nl204550q ↗
14. N. Hansen, Y. Akimoto, and P. Baudis (2019). “CMA-ES/pycma on Github,” Zenodo. 10.5281/zenodo.2559634.
15. Intensity enhancement in textured optical sheets for solar cells10.1109/t-ed.1982.20700 ↗
16. Efficient light trapping in inverted nanopyramid thin crystalline silicon membranes for solar cell applications10.1021/nl2045777 ↗
17. Achieving an accurate surface profile of a photonic crystal for near-unity solar absorption in a super thin-film architecture10.1021/acsnano.6b01875 ↗
18. Broadband moth-eye antireflection coatings on silicon10.1063/1.2870080 ↗
19. Nanophotonic light-trapping theory for solar cells10.1007/s00339-011-6617-4 ↗
20. S4: A free electromagnetic solver for layered periodic structures10.1016/j.cpc.2012.04.026 ↗
21. Fundamental limit of light trapping in grating structures10.1364/oe.18.00a366 ↗
22. Fundamental limit of nanophotonic light trapping in solar cells10.1073/pnas.1008296107 ↗
23. Angular constraint on light-trapping absorption enhancement in solar cells10.1063/1.3532099 ↗
24. Large-area free-standing ultrathin single-crystal sillicon as processable materials10.1021/nl402230v ↗
25. Statistical ray optics10.1364/josa.72.000899 ↗
26. Maximum statistical increase of optical absorption in textured semiconductor films10.1364/ol.8.000491 ↗
27. Forward-mode differentiation of Maxwell’s equations10.1021/acsphotonics.9b01238 ↗
28. Near-optimal methods for minimizing star-convex functions and beyond
29. Accelerated first-order optimization algorithms for machine learning10.1109/jproc.2020.3007634 ↗