Temat: wafer - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Multi-technique characterisation of InAs-on-GaAs wafers with circular defect pattern
Autorzy:: Boguski, Jacek
Wróbel, Jarosław
Złotnik, Sebastian
Budner, Bogusław
Liszewska, Malwina
Kubiszyn, Łukasz
Michałowski, Paweł P.
Ciura, Łukasz
Moszczyński, Paweł
Odrzywolski, Sebastian
Jankiewicz, Bartłomiej
Wróbel, Jerzy
Powiązania:: https://bibliotekanauki.pl/articles/2204219.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Stowarzyszenie Elektryków Polskich
Tematy:: wafer homogeneity
wafer defect pattern
surface roughness
indium arsenide
beryllium doping
Opis:: The article presents the results of diameter mapping for circular-symmetric disturbance of homogeneity of epitaxially grown InAs (100) layers on GaAs substrates. The set of acceptors (beryllium) doped InAs epilayers was studied in order to evaluate the impact of Be doping on the 2-inch InAs-on-GaAs wafers quality. During the initial identification of size and shape of the circular pattern, non-destructive optical techniques were used, showing a 100% difference in average roughness between the wafer centre and its outer part. On the other hand, no volumetric (bulk) differences are detectable using Raman spectroscopy and highresolution X-ray diffraction. The correlation between Be doping level and circular defect pattern surface area has been found.
Źródło:: Opto-Electronics Review; 2023, 31, Special Issue; art. no. e144564
1230-3402
Pojawia się w:: Opto-Electronics Review
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A probabilistic approach for approximation of optical and opto-electronic properties of an opto-semiconductor wafer under consideration of measuring inaccuracy and model uncertainty
Autorzy:: Stroka, Stefan M.
Heumann, Christian
Suhrke, Fabian
Meindl, Kathrin
Powiązania:: https://bibliotekanauki.pl/articles/2204192.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Stowarzyszenie Elektryków Polskich
Tematy:: Gaussian process regression
machine learning
uncertainty quantification
photoluminescence
opto-semiconductor wafer measuring
Opis:: This paper presents a probabilistic machine learning approach to approximate wavelength values for unmeasured positions on an opto-semiconductor wafer after epitaxy. Insufficient information about optical and opto-electronic properties may lead to undetected specification violations and, consequently, to yield loss or may cause product quality issues. Collection of information is restricted because physical measuring points are expensive and in practice samples are only drawn from 120 specific positions. The purpose of the study is to reduce the risk of uncertainties caused by sampling and measuring inaccuracy and provide reliable approximations. Therefore, a Gaussian process regression is proposed which can determine a point estimation considering measuring inaccuracy and further quantify estimation uncertainty. For evaluation, the proposed method is compared with radial basis function interpolation using wavelength measurement data of 6-inch InGaN wafers. Approximations of these models are evaluated with the root mean square error. Gaussian process regression with radial basis function kernel reaches a root mean square error of 0.814 nm averaged over all wafers. A slight improvement to 0.798 nm could be achieved by using a more complex kernel combination. However, this also leads to a seven times higher computational time. The method further provides probabilistic intervals based on means and dispersions for approximated positions.
Źródło:: Opto-Electronics Review; 2023, 31, 2; art. no. e145863
1230-3402
Pojawia się w:: Opto-Electronics Review
Dostawca treści:: Biblioteka Nauki

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