Comprehensive Predictive Device Modeling and Analysis of a Si/Si1- x Ge x Multiquantum-Well Detector

This paper presents a predictive device model implemented by a self-consistent solution of Poisson–Schrödinger drift-diffusion formulation for a thermally sensitive detector based on a [Formula Omitted] multiquantum-well structure. The physical phenomena governing the carrier transport were modeled...

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Bibliographic Details
Published in:IEEE transactions on electron devices 2018-01, Vol.65 (10), p.4353
Main Authors: Atia Shafique, Abbasi, Shahbaz, Ceylan, Omer, Yamamoto, Yuji, Kaynak, Canan Baristiran, Kaynak, Mehmet, Gurbuz, Yasar
Format: Article
Language:English
Subjects:
Carrier transport
Computer simulation
Design parameters
Germanium
Low noise
Mathematical models
Modelling
Performance measurement
Periodic variations
Silicon
Transport phenomena
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Summary:This paper presents a predictive device model implemented by a self-consistent solution of Poisson–Schrödinger drift-diffusion formulation for a thermally sensitive detector based on a [Formula Omitted] multiquantum-well structure. The physical phenomena governing the carrier transport were modeled to investigate the effect of physical design aspects (Ge content, well periodicity, and well thickness). In particular, we have analyzed the effect of these physical design parameters on the carrier dynamics quantified by the dc performance in terms of net current density. A fully integrated simulation framework was developed and employed to optimize Ge content and device doping for a desired figure of merits specified by temperature coefficient of resistance (TCR) and dc resistance ([Formula Omitted]). This methodology was successfully utilized to realize device profiles for various amounts of Ge content and optimization of ([Formula Omitted]) geared for both high TCR and low noise. The dc performance metrics of the optimized profiles obtained by modeling presented here are compared and validated with the fabricated test devices.
ISSN:0018-9383
1557-9646
DOI:10.1109/TED.2018.2862419