2010 12th International Conference on Transparent Optical Networks 2010
DOI: 10.1109/icton.2010.5549299
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Inherent fabrication yields and asymmetries impacts on MZI-SOA static modelling

Abstract: The characteristics of MZI-SOA are affected by its internal design yields and asymmetries, leading to an unbalanced power distribution at the output arms and difficult or non optimum biasing. Therefore, we propose an experimental methodology and model for MZI-SOA structures taking into account internal asymmetries as well as the internal SOA behaviour under different current both as what regards amplitude and phase.

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Cited by 2 publications
(4 citation statements)
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“…The device was characterized experimentally in terms of non-ideal splitting factors and SOA1, 2 gains, following the method adopted in [13].…”
Section: Black Box Static Modelmentioning
confidence: 99%
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“…The device was characterized experimentally in terms of non-ideal splitting factors and SOA1, 2 gains, following the method adopted in [13].…”
Section: Black Box Static Modelmentioning
confidence: 99%
“…A black box model, representing the relation between #I and #J output power as function of input power on port #b, obtained from interferometric structure principles considering the couplers yield, path differences and non-ideality, is given by: Where α is the splitting factor of the last coupler, evaluated experimentally [13]. P 1bi and P 2bi are the power levels on λ b at the input of the rightmost coupler in Fig.…”
Section: Black Box Static Modelmentioning
confidence: 99%
“…To evaluate the behaviour of the MZI-SOA depicted in Fig. 1, we use a black box model [5], based on interferometric structure principles. It also considers the internal couplers yield and path length differences between the upper and lower arms.…”
mentioning
confidence: 99%
“…These issues lead to a very long and difficult initial setup phase [4], which varies from device to device owing to fabrication and integration yields. An MZI-SOA working characteristic is well described with a static model [5], through which we carry out the operational parameters optimisation and the determination of operational limits exploiting a genetic algorithm (GA). To evaluate the behaviour of the MZI-SOA depicted in Fig.…”
mentioning
confidence: 99%