MétaCan
Menu
Back to cohort
Record W2521107426 · doi:10.11159/eee16.132

Electrical Conductivity Extracted From Optical Characterization of Polysilicon Films

2016· article· en· W2521107426 on OpenAlexvenueno aff
B. Birouk, Jean‐Pierre Raskin

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2016
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceCharacterization (materials science)Electrical resistivity and conductivityConductivityOptoelectronicsSiliconElectrical engineeringNanotechnologyChemistryEngineering

Abstract

fetched live from OpenAlex

The purpose of this work is to investigate the optical properties of polycrystalline silicon layers by means of spectroscopic Ellipsometry and other techniques to sustain the analysis (SEM, AFM and Hall effect).The studied MOS structures are composed of c-Si substrate(p-type, Cz<100> oriented), silicon oxide layer (100nm) and polysilicon film (175nm deposited by LPCVD at 625°C) with several doping levels(from 2x1019 to 3x1020 cm-3).A five-layers structure model has been applied, including in addition to MOS structure, a native oxide layer and an effective medium approximation (EMA) roughness layer based on linear growth of air medium. A Cauchy layer model was used to compute the optical parameters (for 400-900 nm wavelength range). The fitting sessions lead to good results as the theoretical and experimental ellipsometric angles curves superposed to each other. The total error was lowered to its minimum during fitting process, by reducing the partial errors on which one can act, like fixed values, misalignment of the angle of incidence [1], bad software convergence, specific measuring error,; and so on. Firstly, the SEM images correlated with AFM ones, of the doped or undoped deposited polysilicon, show sugar loaf shaped surface crystallites, like it appears in reference [2]. The Ellipsometry study showed that the polySi layer roughness has undergone a growth under the effect of the phosphorus thermal diffusion. It increases from 22.2 Å to a value between 23 and 58 Å, depending on the doping level, which agrees with SEM/AFM characterizations and what has been published elsewhere [3,4], in case of the temperature deposition of 625°C. Secondly, one noticed that ellipsometric angles curves were regularly shifted towards low wavelengths when the electrical conductivity increases and the wavelengths gap between the extrema angles, for the same curve, was monotonically decreasing in the same situation. Besides, Psi angle maxima and minima were growing till the doping level reaches a limit (when approaching the phosphorus solubility limit), then started to diminish. On the other hand, the Mean Square Error (MSE) [5,6], when determined between experimental curves, after and before phosphorus diffusion, linearly increases with conductivity enhancement. We took advantage of these curves properties for determining of the electrical conductivity (and resistivity) of the deposited PolySi films, by means of simple relationships between the latter, on the one hand, and the ellipsometric angles extrema and associated wavelengths, on the other hand. Even if this method is not a straightforward manner to extract the conductivity, it remains a good way to avoid electrical contacts on samples, mainly in case of small areas. The evolution of Delta and Psi curves (shift and extrema values variation) can be related to two main influent sources. In the first place, the evolution of the free electrons concentration modifies the complex refractive index in accordance to the Drude theory (correlation between SE and HE measurements). Secondly, the polysilicon layer crystallinity and roughness increase with the doping level, following the thermal budget during the diffusion process. Thirdly, the characterizations confirmed that the refractive index is reduced by the doping process, and that it is all lower as the phosphorus doping level is higher. In addition, we noticed that refractive indices evolution are, in all cases, in agreement with model that has been published in[7], in other words their values decrease on Vis-NIR domain, in accordance with a seven-terms polynomial function n2 = f (λ2), with alternate signs. Furthermore, the seven parameters fall on Gaussian curves and are very depending on the doping and the polysilicon layer thickness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.179
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Electrical Engineering and Computer Systems and ScienceSame topicThin-Film Transistor TechnologiesFrench-language works237,207