Micro-Raman Spectroscopy for the Characterization of Materials in Electronic and Photonic Devices

1997 ◽  
Vol 3 (S2) ◽  
pp. 843-844
Author(s):  
David D.Tuschel

Materials characterization is the primary application of macro- and micro-Raman spectroscopy in our laboratory. Specifically, we wish to correlate chemical bonding and short to long range translational symmetry (including amorphous, highly oriented, polycrystalline, and single crystal materials) to physical, optical and electronic properties of materials and devices. Raman spectroscopy is particularly useful in this capacity because of its origin in the vibrational motions of chemically bonded atoms and its dependence upon crystal symmetry through the polarization selection rules. Furthermore, the high spatial resolution and non-destructive nature of micro-Raman spectroscopy make it ideal for in situcharacterization of electronic and photonic devices. We will present results of materials characterization studies, performed using macro- and micro-Raman spectroscopy, of electronic and photonic devices. In addition, we will discuss how the Raman polarization selection rules can be advantageously applied to device characterization.A primary area of investigation involves the study of ion-implanted and annealed Si by Raman spectroscopy.

1990 ◽  
Vol 5 (2) ◽  
pp. 255-260 ◽  
Author(s):  
P.V. Huong ◽  
A.L. Verma ◽  
J.-P. Chaminade ◽  
L. Nganga ◽  
J.-C. Frison

2018 ◽  
Vol 138 ◽  
pp. 246-254 ◽  
Author(s):  
Haizea Portillo ◽  
Maria Cruz Zuluaga ◽  
Luis Angel Ortega ◽  
Ainhoa Alonso-Olazabal ◽  
Xabier Murelaga ◽  
...  

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1889
Author(s):  
Tiantian Hu ◽  
Hui Song ◽  
Tao Jiang ◽  
Shaobo Li

The two most important aspects of material research using deep learning (DL) or machine learning (ML) are the characteristics of materials data and learning algorithms, where the proper characterization of materials data is essential for generating accurate models. At present, the characterization of materials based on the molecular composition includes some methods based on feature engineering, such as Magpie and One-hot. Although these characterization methods have achieved significant results in materials research, these methods based on feature engineering cannot guarantee the integrity of materials characterization. One possible approach is to learn the materials characterization via neural networks using the chemical knowledge and implicit composition rules shown in large-scale known materials. This article chooses an adversarial method to learn the composition of atoms using the Generative Adversarial Network (GAN), which makes sense for data symmetry. The total loss value of the discriminator on the test set is reduced from 4.1e13 to 0.3194, indicating that the designed GAN network can well capture the combination of atoms in real materials. We then use the trained discriminator weights for material characterization and predict bandgap, formation energy, critical temperature (Tc) of superconductors on the Open Quantum Materials Database (OQMD), Materials Project (MP), and SuperCond datasets. Experiments show that when using the same predictive model, our proposed method performs better than One-hot and Magpie. This article provides an effective method for characterizing materials based on molecular composition in addition to Magpie, One-hot, etc. In addition, the generator learned in this study generates hypothetical materials with the same distribution as known materials, and these hypotheses can be used as a source for new material discovery.


2019 ◽  
Vol 9 (15) ◽  
pp. 3092 ◽  
Author(s):  
Caterina Rinaudo ◽  
Alessandro Croce

Micro-Raman spectroscopy has been applied to fibrous minerals regulated as “asbestos”—anthophyllite, actinolite, amosite, crocidolite, tremolite, and chrysotile—responsible of severe diseases affecting mainly, but not only, the respiratory system. The technique proved to be powerful in the identification of the mineral phase and in the recognition of particles of carbonaceous materials (CMs) lying on the “asbestos” fibers surface. Also, erionite, a zeolite mineral, from different outcrops has been analyzed. To erionite has been ascribed the peak of mesothelioma noticed in Cappadocia (Turkey) during the 1970s. On the fibers, micro-Raman spectroscopy allowed to recognize many grains, micrometric in size, of iron oxy-hydroxides or potassium iron sulphate, in erionite from Oregon, or particles of CMs, in erionite from North Dakota, lying on the crystal surface. Raman spectroscopy appears therefore to be the technique allowing, without preparation of the sample, a complete characterization of the minerals and of the associated phases.


1990 ◽  
Vol 188 ◽  
Author(s):  
Ingrid De Wolf ◽  
Jan Vanhellemont ◽  
Herman E. Maes

ABSTRACTMicro Raman spectroscopy (RS) is used to study the crystalline quality and the stresses in the thin superficial silicon layer of Silicon-On-Insulator (SO) materials. Results are presented for SIMOX (Separation by IMplanted OXygen) and ZMR (Zone Melt Recrystallized) substrates. Both as implanted and annealed SIMOX structures are investigated. The results from the as implanted structures are correlated with spectroscopic ellipsometry (SE) and cross-section transmission electron microscopy (TEM) analyses on the same material. Residual stress in ZMR substrates is studied in low- and high temperature gradient regions.


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