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Record W4241386457 · doi:10.1149/ma2018-02/36/1213

A 2D Empirical Model for On-State Operation of Scaled IGZO TFTs Exemplifying the Physical Response of TCAD Simulation

2018· article· en· W4241386457 on OpenAlexaff
Karl D. Hirschman, Tarun Mudgal, Eli Powell, Robert G. Manley

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsResearch & Development Corporation
Fundersnot available
KeywordsTechnology CADSpiceThreshold voltageChannel (broadcasting)TransistorElectronic engineeringThin-film transistorFigure of meritMaterials scienceVoltageOptoelectronicsComputer scienceElectrical engineeringEngineeringCADNanotechnology

Abstract

fetched live from OpenAlex

The existence of band-tail states (BTS) in indium-gallium-zinc oxide (IGZO) results in TFTs with electrical characteristics that are not well represented by conventional device models. Common parameters such as threshold voltage and channel mobility that are extracted from measured electrical characteristics can be misrepresentative due to discrepancies between the chosen operational model and the underlying device physics. There have been several reports of analytical solutions for the electrostatic operation of IGZO transistors that have been motivated by model accuracy and efficiency in circuit simulation. While developed compact models may be physics based, the addition of fitting parameters to exceedingly complex solutions results in a loss of physical connection and limits the applications to circuit design. Similarly fitting parameters can be added to conventional SPICE models used by device researchers; however the modifications will only have merit if the physical connection to device operation is maintained. This is especially important as channel lengths are scaled and device structures exhibit short channel effects (SCE). A device model for the on-state operation of accumulation-mode IGZO TFTs which maintains consistency with the gradual channel approximation was recently developed and presented as an adaptation of a Level 2 SPICE model. The model accounts for the ionization and deionization of acceptor-like BTS, as controlled by both the gate and drain bias conditions. Long-channel TFT I-V characteristics are well represented by the physical channel length and width along with five operational parameters shown in equations (1-4). The model fit for a representative long-channel device is virtually indistinguishable from measured data, as shown in Fig. 1. The threshold voltage (V T ) and field-independent channel mobility (μ o ) have traditional meaning, whereas the introduced BTS parameters (θ BTS , V BTS , α) regulate the level of free channel charge and account for spreading of the output conductance. Model parameters were extracted using regression analysis on output characteristics with fine gate voltage increments. Parameter values for the representative device are shown in Table 1, with the resulting threshold voltage and channel mobility values consistent with TCAD analogs. The η G and η D model elements preserve the distinction between the gate-impressed and drain-impressed response, respectively, with dissociation from an effective channel mobility. This distinction is important to avoid confounding with SCE as devices are scaled and E-fields are increased. This work extends the device model to represent long-channel and scaled devices (i.e. L ≤ 3 μm) with bottom-gate (BG) and double-gate (DG) electrode configurations. BG and DG TFTs have different levels of gate control and influence over BTS, which becomes more pronounced as the channel length is decreased. For scaled devices, channel length modulation as well as conventional field-effect mobility parameters that account for normal-field degradation and velocity saturation have been added. TCAD simulation is used to discriminate between the influence of BTS and SCE. The physical correlation of the model to device operation is demonstrated through comparisons with measured characteristics and TCAD simulation. 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.044
GPT teacher head0.309
Teacher spread0.265 · 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 designSimulation or modeling
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
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