Experimental Study of a Hybrid Small-Signal Parameter Modeling and Extraction Method for a Microoptoelectronic Device
- Authors
- Han, Jae-Ho; Park, Sung-Woong
- Issue Date
- 12월-2015
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Control systems; frequency response; optoelectronic devices; parameter estimation
- Citation
- IEEE-ASME TRANSACTIONS ON MECHATRONICS, v.20, no.6, pp.3285 - 3290
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE-ASME TRANSACTIONS ON MECHATRONICS
- Volume
- 20
- Number
- 6
- Start Page
- 3285
- End Page
- 3290
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/91687
- DOI
- 10.1109/TMECH.2014.2377736
- ISSN
- 1083-4435
- Abstract
- In the device performance variation and control for microdevices, accurate models are critical for predicting dc behaviors as well as high-frequency behaviors. Thus, acquiring the characteristics of an optoelectronic device is essential for estimating its application to high-throughput control systems. In particular, we present an experimental and analytical investigation for extracting the parameters and intrinsic properties of an optoelectronic microdevice. Our study utilizes a near-infrared, InGaAsP buried heterostructure, multiquantum well distributed-feedback laser. We utilize a modified frequency response model and the conventional method of subtracting frequency responses in two different bias currents above the laser threshold current to attain the resonance frequency and damping factor using a simple four-parameter curve-fitting procedure. With this method, we were able to acquire the intrinsic properties of the laser and its frequency response. In addition, the series resistance, which is drawn directly from a modified current-voltage (I-V) curve, can explicitly reflect the operation of the laser below and above the threshold current. The parasitic capacitance was found by comparing the measured and extracted intrinsic frequency responses. Our extracted results agree well with previously published results.
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Collections - Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
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