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Proceedings Paper

InGaAs MSM photodetectors modeling using DOE analysis
Author(s): Zhaoran Huang; Cheolung Cha; Shuodan Chen; Tomas Sarmiento; Jeng Jung Shen; Nan M. Jokerst; Martin A. Brooke; Gary May; April S. Brown
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Paper Abstract

Linear statistical models have been generated to predict the performance of metal-semiconductor-metal (MSM) PDs for multi-gigabit optical interconnections. The models estimate the bandwidth and responsivity of the MSM PDs based on the input factors: absorbing layer thickness, detector size, finger widths and finger gaps. The design of experiments (DOE) approach was employed to obtain the necessary data to construct the models. Numerous samples were fabricated so that multiple devices measurements could serve to both construct and verify the linear statistical models. The MSM PDs were fabricated from material with structure InAlAs/InAlGaAs/InGaAs (2000Å, 3000Å or 5000Å, absorbing layer)/InAlAs. The MSM interdigitated fingers were photolithographically defined with finger gaps and widths varying as DOE parameters. A benzocyclobutene (BCB, Cyclotene 35) layer was spin-coated onto all of the samples as isolation from the probing pads. In the bandwidth analysis, the detector size (S) and material thickness (T) were investigated with a fixed finger width (1 μm) and gap (1 μm). Taking the measured results of these detectors in the design matrix, and using least square regression, the model equations were derived as: Bandwidth (GHz) = 12.87 - 0.065S - 3T - 0.02ST. After these equations were developed, predictive calculated results from these equations were then further used to predict and compare measured results on devices that were not used in the statistical model. This leads to an average deviation between predicted and measured bandwidth of less than 5%. In the responsivity analysis, the predictive calculation leads to an average deviation less than 11%.

Paper Details

Date Published: 22 January 2004
PDF: 8 pages
Proc. SPIE 5178, Optical Modeling and Performance Predictions, (22 January 2004); doi: 10.1117/12.507337
Show Author Affiliations
Zhaoran Huang, Georgia Institute of Technology (United States)
Cheolung Cha, Georgia Institute of Technology (United States)
Shuodan Chen, Georgia Institute of Technology (United States)
Tomas Sarmiento, Georgia Institute of Technology (United States)
Jeng Jung Shen, Georgia Institute of Technology (United States)
Nan M. Jokerst, Georgia Institute of Technology (United States)
Martin A. Brooke, Georgia Institute of Technology (United States)
Gary May, Georgia Institute of Technology (United States)
April S. Brown, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 5178:
Optical Modeling and Performance Predictions
Mark A. Kahan, Editor(s)

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