AUTHOR=Cruz Carlos , De Vicente Juan TITLE=Charge-coupled device readout by digital-correlated double sampling JOURNAL=Frontiers in Detector Science and Technology VOLUME=2 YEAR=2024 URL=https://www.frontiersin.org/journals/detector-science-and-technology/articles/10.3389/fdest.2024.1487623 DOI=10.3389/fdest.2024.1487623 ISSN=2813-8031 ABSTRACT=

Charge-coupled devices (CCDs) play crucial roles in astronomy owing to their widespread use in the optical band, offering high sensibility, low noise, high dynamic range, and high spatial resolution. Recent advancements in CCDs have focused on improving the sub-electron noise levels for particle detection and enhancing readout speeds through simulation studies. The main objective of this study is to replace the analog processing of CCD signals using the digital-correlated double sampling (DCDS) technique. DCDS allows post-acquisition noise correction through intermittent sampling of an internal reference voltage to provide compact and flexible performance. In this study, DCDS was evaluated using two digital processing systems, namely, a 2.5 mega-samples per second (MSPS) 24-bit analog-to-digital converter (ADC) board and a 250 MSPS 16-bit ADC field-programmable-gate-array( FPGA)-based buffer memory board. The readout noise value at 74 kpix/s (7.1 e) was a significant improvement over that obtained with analog processing (9.7 e). The DCDS implementation demonstrates optimal signal-to-noise ratio for a wide range of readout speeds. Parameters such as the sample positions per pixel, number of samples, and number of pixels were identified to be essential in achieving an accurate gain value. Finally, hardware implementation of the DCDS IP core algorithm on a Xilinx Zynq-7000 AP system-on-a-chip (SoC) showed a significant improvement in power dissipation (7.1 W) compared to the analog Monsoon correlated double sample (CDS) circuit (13.6 W). The DCDS IP core implementation also showed a background noise reduction of up to 34% compared to DCDS offline readout processing using a Python algorithm.