Publications by April S. Brown.

Papers Published

  1. Huang, Zhaoran and Cha, Cheolung and Chen, Shuodan and Sarmiento, Tomas and Shen, J.J. and Jokerst, Nan M. and Brooke, Martin A. and May, Gary and Brown, April S., InGaAs MSM Photodetectors Modeling Using DOE Analysis, Proceedings of SPIE - The International Society for Optical Engineering, vol. 5178 (2004), pp. 148 - 155 [12.507337] .
    (last updated on 2007/04/14)

    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 (2000A, 3000A or 5000A, 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%.

    Keywords:
    Semiconducting indium gallium arsenide;Photolithography;Bandwidth;Field effect transistors;Electric field effects;Optical links;Statistical methods;Optimization;Error analysis;Mathematical models;