Charge Trap Analysis in a SENSEI Skipper-CCD: Understanding Low-Energy Backgrounds in Rare-Event Searches

TL;DR

This work investigates lattice-charge traps in a Skipper-CCD fabricated for SENSEI, focusing on their contribution to low-energy single-electron backgrounds in rare-event searches. By applying pocket-pumping across a wide temperature range, the authors identify and characterize 138 traps in the buried channel, extracting emission times and classifying traps by energy and capture cross section. They fit the emission-time data to a model $\tau_e(T)$ to infer trap parameters $E_t$ and $\sigma$, revealing three trap populations and enabling extrapolations to typical underground operating conditions (≈130 K). A toy Monte Carlo shows that existing masking strategies effectively suppress deferred-charge events from these traps, though slower traps outside current sensitivity may still exist; the results guide background mitigation for future detectors such as Oscura and DAMIC-M.

Abstract

Skipper Charge-Coupled Devices (Skipper-CCDs) are ultra-low-threshold detectors capable of detecting energy deposits in silicon at the eV scale. Increasingly used in rare-event searches, one of the major challenges in these experiments is mitigating low-energy backgrounds. In this work, we present results on trap characterization in a silicon Skipper-CCD produced in the same fabrication run as the SENSEI experiment at SNOLAB. Lattice defects contribute to backgrounds in rare-event searches through single-electron charge trapping. To investigate this, we employ the charge-pumping technique at different temperatures to identify dipoles produced by traps in the CCD channel. We fully characterize a fraction of these traps and use this information to extrapolate their contribution to the single-electron background in SENSEI. We find that this subpopulation of traps does not contribute significantly but more work is needed to assess the impact of the traps that can not be characterized.

Figures (7)

• Figure 1: The Skipper-CCD is mounted in a copper tray that is thermally coupled to a cold finger connected to a cryocooler. The readout electronics is provided by an LTA board. A resistive heater, controlled through a PID feedback loop, allows operation at different temperatures. For uniform illumination, we use an external black-box module that houses an OLED screen controlled by a Raspberry Pi. A diffuser is placed in front of an opening aligned with a fused-silica window on the vacuum vessel, allowing light to reach the Skipper-CCD.
• Figure 2: Diagram of the dipole detection algorithm. From left to right: the computation of the self-correlation, the filtering by a threshold value $C$, and the symmetry filter before confirming a detection. $q_{1}$ and $q_{2}$ denote the charges measured in the two adjacent pixels along the pumping direction.
• Figure 3: Measured dipole intensity as a function of $t_{ph}$ for two detected traps: one appearing at low temperatures (top) and another detectable at high temperatures (bottom). Data are shown for several temperatures, along with their corresponding fits using Eq. \ref{['I_fit']}.
• Figure 4: Measured dipole intensity as a function of $t_{ph}$ for two detected traps (top and bottom) that showed two distinct responses at low temperatures ($<150$K) and again at high temperature $>170$K). Data are shown for several temperatures, along with their corresponding fits using Eq. \ref{['I_fit']}.
• Figure 5: Distribution of the distance between identified traps on the CCD. We also show the mean and the 90% interval of toy Monte Carlo simulations assuming traps are uniformly distributed