Investigation of the usability of machine learning algorithms in determining the specific electrical parameters of Schottky diodes

Güzel, Tamer; Çolak, Andaç Batur

doi:10.1016/j.mtcomm.2022.104175

Cited by 10 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 20–22 ] MAE, MSE, and RMSE functions give current values and their units are ampere (A).

MAE = \frac{1}{n} \sum_{i = 1}^{n} | I_{e_{i}} - I_{{est}_{i}} |

MSE = \frac{1}{n} \sum_{i = 1}^{n} {(I_{e_{i}} - I_{{est}_{i}})}^{2}

RMSE = \sqrt{MSE} = \sqrt{\frac{1}{n} \sum_{i = 1}^{n} {(I_{e_{i}} - I_{{est}_{i}})}^{2}}

where n is the data sample number, I e is the experimental current, and I est is the estimated current by regression models. Also, the other performance indicator is R and it is given as [ 23 ]

R = \sqrt{1 - \frac{\sum_{i = 1}^{n} {false(I_{e_{i}} - I_{{est}_{i}} false)}^{2}}{\sum_{i = 1}^{n} {false(I_{e_{i}} false)}^{2}}}

…”

Section: Methodsmentioning

confidence: 99%

“…The general I–V formula of SBD can be suggested as [ 23,26,40 ]

I = A A^{*} T^{2} exp (- \frac{q ϕ_{normalb}}{k T}) [exp (\frac{q (V - I R_{normals})}{n k T}) - 1]

where

A A^{*} T^{2} exp (- \frac{q ϕ_{normalb}}{k T})

is the saturation current I 0 . A* is the effective Richardson constant and equals to 8.16 A cm −2 K 2 for n‐type GaAs, A is the diode area (1,767 × 10 −2 cm), and T is the ambient temperature in Kelvin.…”

Section: Methodsmentioning

confidence: 99%

“…where n is the data sample number, I e is the experimental current, and I est is the estimated current by regression models. Also, the other performance indicator is R and it is given as [23] R ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi…”

Section: Modeling Of I-v Characteristic Of Ni/n-gaas/in Sbdmentioning

confidence: 99%

“…For the aforementioned reasons, to analyze the error performance of proposed studies, the calculation of MAE, MSE, RMSE, and R values are frequently used by many researchers. [15,20,23,27,29]…”

Section: Modeling Of I-v Characteristic Of Ni/n-gaas/in Sbdmentioning

confidence: 99%

“…The margins of error for the ideality factor ( n ) and the series resistance ( R s ) were found to be 1.645 and 5.694, respectively. [ 23 ] Olikh studied the parameter estimation of SBD using I–V characteristics. Barrier height ( ϕ b ), n , and R s parameters were estimated using ten analytical methods, four optimization algorithms, and two analytical methods.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Next‐Generation Application‐Based Artificial Intelligence in Modeling and Estimation for Ni/n‐GaAs/In Schottky Barrier Diode

Doğan

Koçkanat

2023

Physica Status Solidi (a)

View full text Add to dashboard Cite

Herein, for Ni/n-GaAs/In Schottky barrier diode, experimental measurement, modeling, data generation from the model, and parameter estimation processes are simultaneously carried out. In the experimental step, Ni/n-GaAs/In Schottky barrier diodes are fabricated and annealed from the temperature of 200 °C up to 600 °C with 100 °C steps. Current values are recorded by applying voltage to the diode contacts from À1 V up to 0.5 V. In the modeling step, 1503 experimental current-voltage data are used for 19 different regression models. For Adaptive Neuro Fuzzy System (ANFIS), when root mean square error, mean square error, mean absolute error, and coefficient of determination are calculated 6.0341e-07, 3.6410e-13, 2.3873e-07, and 0.9999 for training, they are obtained 5.8904e-07, 3.4697e-13, 2.3083e-07, and 0.9999 for testing. In the estimation step, the values of electrical parameters are estimated by using Mayfly algorithm. Estimations are performed for all annealing temperatures. In addition, current-voltage data for the annealing temperature of 350 °C are produced by the ANFIS model. Thus, a new-generation artificial intelligence application, that includes measurement, modeling, and estimation for the Ni/n-GaAs/In Schottky barrier diode with varying annealing temperatures, is realized and a new perspective is provided to researchers and practitioners.

show abstract

“…[ 20–22 ] MAE, MSE, and RMSE functions give current values and their units are ampere (A).

MAE = \frac{1}{n} \sum_{i = 1}^{n} | I_{e_{i}} - I_{{est}_{i}} |

MSE = \frac{1}{n} \sum_{i = 1}^{n} {(I_{e_{i}} - I_{{est}_{i}})}^{2}

RMSE = \sqrt{MSE} = \sqrt{\frac{1}{n} \sum_{i = 1}^{n} {(I_{e_{i}} - I_{{est}_{i}})}^{2}}

where n is the data sample number, I e is the experimental current, and I est is the estimated current by regression models. Also, the other performance indicator is R and it is given as [ 23 ]

R = \sqrt{1 - \frac{\sum_{i = 1}^{n} {false(I_{e_{i}} - I_{{est}_{i}} false)}^{2}}{\sum_{i = 1}^{n} {false(I_{e_{i}} false)}^{2}}}

…”

Section: Methodsmentioning

confidence: 99%

“…The general I–V formula of SBD can be suggested as [ 23,26,40 ]

I = A A^{*} T^{2} exp (- \frac{q ϕ_{normalb}}{k T}) [exp (\frac{q (V - I R_{normals})}{n k T}) - 1]

where

A A^{*} T^{2} exp (- \frac{q ϕ_{normalb}}{k T})

Section: Methodsmentioning

confidence: 99%

Section: Modeling Of I-v Characteristic Of Ni/n-gaas/in Sbdmentioning

confidence: 99%

Section: Modeling Of I-v Characteristic Of Ni/n-gaas/in Sbdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Next‐Generation Application‐Based Artificial Intelligence in Modeling and Estimation for Ni/n‐GaAs/In Schottky Barrier Diode

Doğan

Koçkanat

2023

Physica Status Solidi (a)

View full text Add to dashboard Cite

show abstract

Modeling of Darcy‐Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural network

Shafiq

Çolak

Sindhu

2023

Numerical Methods in Fluids

Self Cite

View full text Add to dashboard Cite

Modern industries face a new challenge in cooling processes. Traditional cooling lubricants have limited heatconducting capacity. The development of nanofluids possessing superior properties such as high thermal conductivity, homogeneity, and long‐term stability has revolutionized the cooling lubrication industry. The literature reports a wide range of applications of nanofluid, such as cooling devices, peristaltic pumps for diabetic treatments, accelerators, reactors, petroleum industry applications, solar collectors and so forth. Nanofluids like Williamson nanofluid are very important non‐Newtonian fluids that have pseudoplastic properties. Williamson nanofluid has a number of applications in the medical and engineering sciences. It is used in food processing, inkjet printing, adhesives and emulsions, coated photographic films, and many other applications. In the current study, the nanomaterial flow of the Darcy‐Forchheimer Williamson nanofluid model is evaluated using the Levenberg–Marquardt approach with backpropagated neural networks. Thermalphoresis and Brownian motion are incorporated into the nanofluid model. This system is converted into an analogous nonlinear ordinary differential system through the application of necessary transformations. A dataset for the proposed multilayer perceptron artificial neural network is generated by altering the necessary variables through a Runge–Kutta fourth‐order shooting procedure. It has been created an artificial neural network called a multiple‐layer perceptron in order to forecast the values of the multiple‐layer perceptron. It is discovered that the parameter had the highest mean deviation of and the parameter had the lowest mean deviation of . Furthermore, MSE value of ANN model developed to estimate the skin friction coefficient value as and R value as 0.99954 whereas MSE and R values of the ANN model developed for the estimation of the LNN value were obtained as and 0.99999, respectively.

show abstract

Efficient SnS Solar Cells via Plasmonic Light Trapping and Alternative Buffer Layers: A Combined Machine Learning and FDTD Analysis

Ferhati,

Berghout,

Djeffal

2024

Plasmonics

View full text Add to dashboard Cite

Investigation of the usability of machine learning algorithms in determining the specific electrical parameters of Schottky diodes

Cited by 10 publications

References 35 publications

Next‐Generation Application‐Based Artificial Intelligence in Modeling and Estimation for Ni/n‐GaAs/In Schottky Barrier Diode

Next‐Generation Application‐Based Artificial Intelligence in Modeling and Estimation for Ni/n‐GaAs/In Schottky Barrier Diode

Modeling of Darcy‐Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural network

Efficient SnS Solar Cells via Plasmonic Light Trapping and Alternative Buffer Layers: A Combined Machine Learning and FDTD Analysis

Contact Info

Product

Resources

About