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Record W4235261563 · doi:10.1149/ma2018-02/36/1200

Integration Challenges of Flash Lamp Annealed LTPS for High Performance CMOS TFTs

2018· article· en· W4235261563 on OpenAlexaff
Glenn Packard, Adam Rosenfeld, Paul Bischoff, Karthik Bhadrachalam, Viraj Garg, Robert G. Manley, Karl D. Hirschman

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsResearch & Development Corporation
Fundersnot available
KeywordsFlash-lampMaterials scienceOptoelectronicsAmorphous solidAmorphous siliconDopant ActivationPassivationPolycrystalline siliconAnnealing (glass)CMOSSiliconDopantNanotechnologyEngineering physicsCrystalline siliconThin-film transistorDopingMetallurgyLayer (electronics)

Abstract

fetched live from OpenAlex

The development of low-temperature polycrystalline silicon (LTPS) based on excimer laser annealing (ELA) has realized CMOS TFTs with notable electrical performance. The flat-panel display industry is searching for alternative LTPS strategies which are cost-effective and easily scalable to large glass panel production, which has led to recent interest in the application of flash-lamp annealing (FLA) for the LTPS crystallization process. The FLA-LTPS process exposes large areas of amorphous silicon with high irradiance from xenon flashlamps for pulse durations that are in microsecond timescale. Amorphous silicon absorbs a sufficient portion of the xenon emission spectrum to melt and crystallize into polysilicon while staying within the thermal constraints of the underlying glass substrate. Multi-lamp exposure systems with high repetition pulse rates would potentially offer significant advantages in manufacturing throughput and cost. The application of FLA-LTPS for CMOS TFTs has been recently reported, with best-case performance comparable to ELA-LTPS. Investigations to improve upon this technology are in progress to address integration challenges to reduce variation in device operation and enable device scaling. The focus areas of this work are material uniformity, source/drain dopant activation, and defect passivation; all of which are fundamental issues facing FLA-LTPS TFTs. The importance of patterning the amorphous silicon film into separate super-mesa elements prior to FLA crystallization was previously established. The FLA system for this work was a NovaCentrix PulseForge 3300 system, used to crystallize polygons of a-Si with a 100 nm SiO 2 capping layer. The FLA response of densely packed mesa arrays exhibits a proximity effect which impedes the melting of interior mesas, whereas exterior mesas demonstrate a crystallization response consistent with isolated mesas. This comparison is highlighted in Fig. 1 by the pronounced gradient in crystallization morphology. These observations have led to a theory of competing rates between solid-phase and liquid-phase crystallization, hypothesized due to the large difference between the melting points of amorphous and crystalline silicon. The challenge of dopant activation is exacerbated by the need to limit lateral diffusion. Dopants within silicon that has entered a liquid phase will experience dramatically enhanced diffusion, even within the ultra-short pulse duration of the FLA process. The result is a compromised intrinsic channel which limits device scaling. Thus, dopants must be introduced into post-FLA LTPS and activated at glass-compatible temperatures, which is also a requirement for self-aligned devices. Solid-phase dopant activation in FLA-LTPS films has been investigated using furnace annealing and multiple shot FLA exposures at lower intensity. Pre-amorphizing the source/drain regions with electrically inactive species has also been explored. Hydrogen passivation treatments using both plasma and sintering processes have been investigated in efforts to reduce the influence of defect states. Pre-amorphization and passivation processes have yielded promising device characteristics with low series resistance, low leakage current, and high carrier mobility, as shown in Fig. 2. Figure 1

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 score0.050
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.220
Teacher spread0.202 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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