Distilling Nanoscale Heterogeneity of Amorphous Silicon using Tip-enhanced Raman Spectroscopy (TERS) via Multiresolution Manifold Learning

Abstract Accurately identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon (a-Si) is crucial for accelerating technology development. However, short-range atomic ordering quantification and nanoscale spatial resolution over a large area on a-Si have remained major challenges and practically unexplored. We resolve phonon vibrational modes of a-Si at a lateral resolution of 20 nm by tip-enhanced Raman spectroscopy (TERS). To project the high dimensional TERS imaging to a low dimensional (i.e. 2D) manifold space and categorize a-Si structure, we developed a multiresolution manifold learning (MML) algorithm. It allows for quantifying average Si-Si distortion angle and the strain free energy at nanoscale without a human-specified threshold. The MML multiresolution feature allows for distilling local defects of ultra-low abundance (< 0.3%), presenting a new Raman mode at finer resolution grids. This work promises a general paradigm of resolving nanoscale structural heterogeneity and updating domain knowledge for highly disordered materials.

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Distilling nanoscale heterogeneity of amorphous silicon using tip-enhanced Raman spectroscopy (TERS) via multiresolution manifold learning

Nature Communications ◽

10.1038/s41467-020-20691-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Guang Yang ◽

Xin Li ◽

Yongqiang Cheng ◽

Mingchao Wang ◽

Dong Ma ◽

...

Keyword(s):

Raman Spectroscopy ◽

Amorphous Silicon ◽

Manifold Learning ◽

Domain Knowledge ◽

Amorphous Materials ◽

Learning Algorithm ◽

Structural Heterogeneity ◽

Dimensional Manifold ◽

Large Area ◽

Tip Enhanced Raman Spectroscopy

AbstractAccurately identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon is crucial for accelerating technology development. However, short-range atomic ordering quantification and nanoscale spatial resolution over a large area on a-Si have remained major challenges and practically unexplored. We resolve phonon vibrational modes of a-Si at a lateral resolution of <60 nm by tip-enhanced Raman spectroscopy. To project the high dimensional TERS imaging to a two-dimensional manifold space and categorize amorphous silicon structure, we developed a multiresolution manifold learning algorithm. It allows for quantifying average Si-Si distortion angle and the strain free energy at nanoscale without a human-specified physical threshold. The multiresolution feature of the multiresolution manifold learning allows for distilling local defects of ultra-low abundance (< 0.3%), presenting a new Raman mode at finer resolution grids. This work promises a general paradigm of resolving nanoscale structural heterogeneity and updating domain knowledge for highly disordered materials.

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Learning Manifolds

Machine Learning in Computer-Aided Diagnosis - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-0059-1.ch018 ◽

2012 ◽

pp. 374-402

Author(s):

Diana Mateus ◽

Christian Wachinger ◽

Selen Atasoy ◽

Loren Schwarz ◽

Nassir Navab

Keyword(s):

Manifold Learning ◽

Domain Knowledge ◽

Dimensional Space ◽

Human Motion ◽

Motion Modeling ◽

Learning Methods ◽

Data Representations ◽

Non Linear ◽

Data Points ◽

Low Dimensional

Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-points, manifold learning algorithms first approximate the low dimensional manifold where data lives with a graph; then, they find a non-linear map to embed this graph into a low dimensional space. Since the explicit pairwise relations and the neighborhood system can be designed according to the application, manifold learning methods are very flexible and allow easy incorporation of domain knowledge. The authors describe different assumptions and design elements that are crucial to building successful low dimensional data representations with manifold learning for a variety of applications. In particular, they discuss examples for visualization, clustering, classification, registration, and human-motion modeling.

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Probing Bilayer Grain Boundaries in Large-Area Graphene with Tip-Enhanced Raman Spectroscopy

Advanced Materials ◽

10.1002/adma.201603601 ◽

2016 ◽

Vol 29 (7) ◽

pp. 1603601 ◽

Cited By ~ 18

Author(s):

Kyoung-Duck Park ◽

Markus B. Raschke ◽

Joanna M. Atkin ◽

Young Hee Lee ◽

Mun Seok Jeong

Keyword(s):

Raman Spectroscopy ◽

Grain Boundaries ◽

Large Area ◽

Tip Enhanced Raman Spectroscopy

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Graphene: Probing Bilayer Grain Boundaries in Large-Area Graphene with Tip-Enhanced Raman Spectroscopy (Adv. Mater. 7/2017)

Advanced Materials ◽

10.1002/adma.201770048 ◽

2017 ◽

Vol 29 (7) ◽

Author(s):

Kyoung-Duck Park ◽

Markus B. Raschke ◽

Joanna M. Atkin ◽

Young Hee Lee ◽

Mun Seok Jeong

Keyword(s):

Raman Spectroscopy ◽

Grain Boundaries ◽

Large Area ◽

Tip Enhanced Raman Spectroscopy

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Computer simulation study about the dependence of amorphous silicon photonic waveguides efficiency on the material quality

The European Physical Journal Applied Physics ◽

10.1051/epjap/2020190250 ◽

2020 ◽

Vol 90 (3) ◽

pp. 30502

Author(s):

Alessandro Fantoni ◽

João Costa ◽

Paulo Lourenço ◽

Manuela Vieira

Keyword(s):

Amorphous Silicon ◽

High Throughput Screening ◽

Simulation Analysis ◽

Low Cost ◽

Deposition Process ◽

Large Area ◽

Sidewall Roughness ◽

Sensing Applications ◽

Fabrication Defects ◽

The Impact

Amorphous silicon PECVD photonic integrated devices are promising candidates for low cost sensing applications. This manuscript reports a simulation analysis about the impact on the overall efficiency caused by the lithography imperfections in the deposition process. The tolerance to the fabrication defects of a photonic sensor based on surface plasmonic resonance is analysed. The simulations are performed with FDTD and BPM algorithms. The device is a plasmonic interferometer composed by an a-Si:H waveguide covered by a thin gold layer. The sensing analysis is performed by equally splitting the input light into two arms, allowing the sensor to be calibrated by its reference arm. Two different 1 × 2 power splitter configurations are presented: a directional coupler and a multimode interference splitter. The waveguide sidewall roughness is considered as the major negative effect caused by deposition imperfections. The simulation results show that plasmonic effects can be excited in the interferometric waveguide structure, allowing a sensing device with enough sensitivity to support the functioning of a bio sensor for high throughput screening. In addition, the good tolerance to the waveguide wall roughness, points out the PECVD deposition technique as reliable method for the overall sensor system to be produced in a low-cost system. The large area deposition of photonics structures, allowed by the PECVD method, can be explored to design a multiplexed system for analysis of multiple biomarkers to further increase the tolerance to fabrication defects.

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