(Invited) Evolutionary Parameter Extraction for Organic TFT Compact Models Including Contact Effects
Bibliographic record
Abstract
The development of accurate and computationally efficient models is critical to reduce the cycle time between design, manufacturing and characterization of electronic devices, circuits and systems. The models must include degrading effects in order to better describe the performance of manufactured device. This is the case of contact effects in the modelling of organic thin film transistors (OTFTs). In fact, much effort was made to include contact effects into compact models. In this work, we consider a generic analytical model for the current-voltage (ID-VD) characteristics of OTFTs, valid for all the operation regions of the transistor, including the subthreshold region [1]. This model was later redefined with the inclusion of a model for the current-voltage (ID-VC) curves of the contact region, and a parameter extraction procedure, in which a sequence of iterative steps is carried out until a good agreement between experimental and simulated current voltage curves is obtained [2]. The parameter extraction procedure was accelerated in [3] with the proposal of an evolutionary procedure to extract the parameters that best fit experimental current-voltage characteristics. Later in [4], improvements over this evolutionary procedure were presented. Due to the explosion of different applications of OTFTs such as phototransistors and a wide variety of physical and chemical sensors, novel materials, contacts, and designs for transistor were proposed and used. For these applications, more accurate models and better extraction parameter procedures are needed. These new structures impose more requirements to the extraction methods and models, and therefore to the extracted parameters. From this point of view, the evolutionary extraction procedures proposed in [3, 4] are a good option to be used in combination with the above mentioned generic analytical model for the current-voltage (ID-VD) characteristics of OTFTs [1, 2]. In order to adapt both the compact model and the extraction procedure to new applications of the OTFTs, rules are added in form of optimization objectives and constrains for the different parameters. In this work, we present such rules applied to different sets of thin film phototransistors and sensors. In the first place, we consider the multi-objective evolutionary algorithm (MOEA), NSGA-II [5]. It was used in [3, 4], and seeks values of the parameter in the compact model that best meet some user defined objectives. In [3], two objectives were optimized, while in [4], four objectives were optimized, along with some defined constraints. It is known that classic MOEAs, such as the NSGA-II, have serious limitations when coping with more than three objectives. These problems are referred as many-objective optimization problems (MaOPs). Among the limitations of MOEAs to treat MaOPs are the selection operators, computational cost, visualization of the Pareto optimal front (POF), and more importantly, the convergence to an optimal solution. Recently, a many-objective implementation of the NSGA-II, the NSGA-III, was released [5]. NSGA-III was designed to specially deal with MaOPs, incorporating different operators to the ones used by its predecessor. Thus, in a second part of this work, we substitute the NSGA-II algorithm with the NSGA-III one for the characterization of the same sets of OTFTs. Finally, the results of using both algorithms are compared and will be discussed. Acknowledgments This work was supported by projects MAT2016-76892-C3-3-R and TIN2015-67020-P funded by the Spanish Government, European Regional Development Funds (ERDF) and the Canada Research Chair Program. References [1] O. Marinov, M. J. Deen, U. Zschieschang, H. Klauk, Organic thin-film transistors: Part I-compact dc modeling, IEEE Trans. Electron Devices 56 (2009) 2952–2961. [2] J. A. Jiménez Tejada, J. A. López Villanueva, P. López Varo, K. M. Awawdeh, M. J. Deen, Compact modeling and contact effects in organic transistors, IEEE Trans. Electron Devices 61 (2) (2014) 266–277. [3] A. Romero, J. González, R. Picos, M. J. Deen, J. A. Jiménez-Tejada, Evolutionary parameter extraction for an organic TFT compact model including contact effects, Organic Electronics 61 (2018) 242-253. [4] A. Romero, J. González, J.A. Jiménez-Tejada, Constrained Many-Objective Evolutionary Extraction Procedure for an OTFT Compact Model including Contact Effects, in: Spanish Conference on Electron Devices, 2018. [5] K. Deb, H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evolutionary Computation 18(4) (2014) 577-601.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".