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Modulation transfer function and quantum efficiency correlation at long wavelengths (greater than 800 nm) in linear
Applied Optics
|June 16, 2010
Summary
The spectral modulation transfer function (MTF) of commercial imagers was compared to theory. Carrier lifetime significantly impacts both MTF and quantum efficiency (QE) in silicon charge coupled imagers (CCIs).
Area of Science:
- Optoelectronics
- Solid-state physics
- Image sensor technology
Background:
- Commercial linear silicon charge coupled imagers (CCIs) are widely used in imaging applications.
- Understanding their performance characteristics, such as spectral modulation transfer function (MTF), is crucial for optimizing image quality.
- Existing MTF theories often need refinement to accurately model real-world device behavior.
Purpose of the Study:
- To measure and compare the spectral MTF of multiple identical commercial CCIs.
- To develop and validate an MTF theory incorporating detector aperture and lateral carrier diffusion.
- To investigate the correlation between spectral MTF, quantum efficiency (QE), and minority carrier lifetime.
Main Methods:
- Experimental measurement of spectral MTF for several commercial CCIs.
- Theoretical modeling of MTF, including detector aperture effects and one-dimensional lateral carrier diffusion.
- Correlation analysis of spectral MTF, QE, and minority carrier lifetime across different CCIs.
- Model parameterization using carrier lifetime as a variable.
Main Results:
- A strong agreement was achieved between experimental MTF data and theoretical predictions with appropriate model parameters.
- A significant correlation was observed between long-wave (> 800-nm) MTF and spectral quantum efficiency (QE) across the tested CCIs.
- Minority carrier lifetime was identified as a critical parameter influencing both MTF and QE.
Conclusions:
- The developed MTF theory accurately describes the performance of commercial CCIs.
- Minority carrier lifetime is a key determinant of both spectral MTF and QE in silicon CCIs.
- This understanding facilitates improved design and performance prediction for imaging devices.

