Model-independent Characterization of Charge Diffusion in Thick Fully Depleted CCDs

Details Model-independent Characterization of Charge Diffusion in Thick Fully Depleted CCDs Model-independent Characterization of Charge Diffusion in Thick Fully Depleted CCDs

Sep 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011pasp..123.1100l&link_type=abstract

Publications of the Astronomical Society of the Pacific, Volume 123, issue 907, pp.1100-1106

Statistics

Computation

Astronomical Instrumentation

Scientific paper

We present a new method to measure charge diffusion in charge-coupled devices (CCDs). The method is based on a statistical characterization of the shapes of charge clouds produced by low-energy X-rays using known properties of the two-dimensional Gaussian point-spread function (PSF). The algorithm produces reliable upper and lower bounds on the size of the PSF for photons converting near the entrance window of a device. It is optimally suited to the case of thick back-illuminated CCDs where the X-ray absorption length is smaller than the silicon thickness and the diffusion scale is of the same order as the pixel size. The only assumptions are that the charge cloud width is a monotonically increasing function of distance from the conversion point to the buried channel, and that the conversion probability is peaked at the surface. Otherwise, no physical models of carrier transport or knowledge of the electric field profile in the CCD are needed. In suboptimal conditions, the upper bound increases and the lower bound is unaffected, so confidence in the correctness of results is retained. The new method has been benchmarked against Monte Carlo simulations and tested on X-ray images measured on thick high-resistivity prototype CCDs for the Large Synoptic Survey Telescope. In Monte Carlo simulations of noiseless images having the optimal diffusion scale, the upper bound approximated the true PSF within 5%, increasing to 10% in simulations with added noise. Even with severely undersampled or truncated PSFs, the method brackets the true value to within 25%. Our method is accurate and computationally efficient and offers a fast and simple experimental setup.

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