Sensitivity Analysis of Si-Avalanche Photodiode for Noise Performance Assessment

Silicon avalanche photodiodes (APDs) play a key role in low-photon count electromagnetic wave detection applications, including single-photon (SPAD), night vision, and light detection and ranging. There is enormous potential to perfect existing APD designs by addressing limitations such as high-voltage breakdown, response time, and poor noise performance. We present a sensitivity-analysis-based method to capture the impact of temperature, bias voltage, and illumination power fluctuations on the noise performance of APDs. The study uses a well-calibrated ATLAS Silvaco framework utilizing our inhouse experimental data. Using this well-calibrated framework, we study the impact of temperature, bias voltage, and illumination power variation on the response time of the APD. The dark current of APDs has an exponential relationship with temperature. In other studies, this increases the magnitude of dark current by 3–4 orders over a 100°C interval range. We show that a change of 10°C in temperature results in a ~28% change in the fall time, a parameter that defines the operational 3dB bandwidth of the APD. The variation in bias voltage and illumination power is shown to impact the intensity of the response with ~3% variation in the fall time. A study of this nature would enable the robust design of APD devices, ensuring reliable and efficient photon detection under low-light conditions.

Authors

Institutions

Publication Details

Published
2024-10-21
DOI
https://doi.org/10.1109/nmdc58214.2024.10894206
Citations
1
Primary Topic
Photonic and Optical Devices
Type
article
Field-Weighted Citation Impact
0.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sensitivity Analysis of Si-Avalanche Photodiode for Noise Performance Assessment

M. Saif Islam, Amita Rawat, Zbynka Kekula
1 citations
Photonic and Optical Devices
0.21
article

Sensitivity Analysis of Si-Avalanche Photodiode for Noise Performance Assessment

M. Saif Islam, Amita Rawat, Zbynka Kekula
article en
1 citations

Abstract

Silicon avalanche photodiodes (APDs) play a key role in low-photon count electromagnetic wave detection applications, including single-photon (SPAD), night vision, and light detection and ranging. There is enormous potential to perfect existing APD designs by addressing limitations such as high-voltage breakdown, response time, and poor noise performance. We present a sensitivity-analysis-based method to capture the impact of temperature, bias voltage, and illumination power fluctuations on the noise performance of APDs. The study uses a well-calibrated ATLAS Silvaco framework utilizing our inhouse experimental data. Using this well-calibrated framework, we study the impact of temperature, bias voltage, and illumination power variation on the response time of the APD. The dark current of APDs has an exponential relationship with temperature. In other studies, this increases the magnitude of dark current by 3–4 orders over a 100°C interval range. We show that a change of 10°C in temperature results in a ~28% change in the fall time, a parameter that defines the operational 3dB bandwidth of the APD. The variation in bias voltage and illumination power is shown to impact the intensity of the response with ~3% variation in the fall time. A study of this nature would enable the robust design of APD devices, ensuring reliable and efficient photon detection under low-light conditions.

University of California, Davis (US)
National Science Foundation
Climate action
Openalex Percentile: Top 43%
Photonic and Optical Devices
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.