研究目的
Investigating the enhanced surface passivation of predictable quantum efficient detectors by silicon nitride and silicon oxynitride/silicon nitride stack.
研究成果
The oxynitride stack and nitride monolayers exhibit excellent passivating properties, making them good candidates for use in self-induced photodiodes. Device simulations predict an internal quantum deficiency well below 1 ppm for certain wavelengths, indicating their potential for highly accurate predictable quantum efficient detectors.
研究不足
The study is limited by the high resistivity of the substrate affecting the accuracy of C-V measurements and the potential for current leakages through pinholes in larger electrodes during soaking experiments. Additionally, the absorption coefficients of the films were associated with high uncertainty due to the measurement setup.
1：Experimental Design and Method Selection:
The study involved the deposition of three different passivating films on high resistivity silicon substrates using high-frequency plasma-enhanced chemical vapor deposition (PECVD). The films were characterized using conventional methods and the novel method of photoluminescence imaging under applied bias (PL-V) and high voltage soaking to modulate the fixed charge density Qf in the layers.
2：Sample Selection and Data Sources:
The substrates were 4 in. high-quality polished p-type [100] monocrystalline Float Zone wafers with a nominal thickness of 525 μm and a resistivity of >10,000 Ω cm.
3：List of Experimental Equipment and Materials:
Oxford instruments PlasmaLab 133 direct plasma system for PECVD, WCT-120 setup from Sinton Instruments for QSSPC measurements, four probe Keithley 4200-SCS semiconductor characterization system for C-V measurements, LIS-R1 PL imaging setup from BT imaging for PL imaging.
4：Experimental Procedures and Operational Workflow:
The wafers were treated with HF solution before layer deposition. The passivation layers were deposited at 400 °C. After deposition, the samples were annealed and characterized using QSSPC, C-V, and PL-V measurements.
5：Data Analysis Methods:
The data were analyzed using Cogenda Genius TCAD for simulations, fitting the experimental data to model the surface recombination properties and predict the internal quantum deficiency of photodiodes.
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