(Digital Presentation) Temporal Evolution of the Threshold Voltage in Organic Thin Film Memory Transistors
Bibliographic record
Abstract
We present a procedure to monitor the temporal evolution of the trapped charge in floating gates of organic thin film transistors (OTFTs) based memories in dynamic regimes. The potential applications of organic semiconductors in flexible and wearable electronics and their known features such as low-temperature and solution fabrication make the OTFTs be considered as candidates also in the field of non-volatile memories. Past efforts have been devoted on the preparation and optimization of floating gates [1]. In the present work, we focus on the modelling of this kind of devices, in particular, on a model for the threshold voltage useful in dynamic experiments, such as hysteresis and transient regimes. The information extracted from the modelling and simulation of these devices can be related to technological parameters with the objective of finding a better memory design. Current-voltage curves impacted by hysteresis are usually reproduced assuming the threshold voltage to be constant or piecewise constant. Nevertheless, despite positive outcomes [2], this is not a precise approach since it does not consider the actual temporal evolution of the threshold voltage. Thus, a time dependent threshold voltage is mandatory in order to interpret hysteresis phenomena and transient experiments, which in turn can be associated to trapping and de-trapping mechanisms over time. In this work, a model that describes the evolution of the threshold voltage with the time, which is linked to the variation of the trapped charge in the channel, is proposed. The evolution of the trapped charge is described with a first-order linear differential equation with a term controlled by a time constant t, and another term, similar to the generation term in a typical continuity equation, which is proportional to the drain current flowing through the transistor channel. This model is introduced in a compact model previously developed for OTFTs that include contact effects [3–6]. The resulting model is combined with an evolutionary parameter extraction procedure, which is applied to experimental current–voltage curves taken from the literature that show both contact and hysteresis effects [7]. Figures (a) and (b) show the best fitting of our calculations (solid lines) and experimental output and transfer characteristics (symbols), respectively, of a pentacene-based organic thin film memory transistor using poly (methyl methacrylate) (PMMA) as the insulator [7]. Figures (c) and (d) show the time evolution of the threshold voltage during the experiments (a) and (b), respectively. The authors acknowledge support from the project PID2022-139586NB-44 funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR. References [1] T. Xu, et al., Appl. Phys. Lett. , 116 (2), 023301 (2020). [2] K. M. Awawdeh, et al., Org. Electron . 14 (12), 3286–3296 (2013). [3] O. Marinov, et al., IEEE Trans. Electron Devices, 56 (12) 2952–2961 (2009). [4] J. A. Jiménez-Tejada, et al., IEEE Trans. Electron Devices, 61 (2), 266–277 (2014). [5] A. Romero, et al., Org. Electron . 77 , 105523 (2020). [6] A. Romero, et al., Org. Electron . 92, 106129 (2021). [7] S. Fakher, “Advanced study of pentacene-based organic memory structures,” Ph.D. dissertation, (2014). Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".