Estimation of optical constants of thin film by the use of artificial neural networks

1996 ◽  
Vol 35 (25) ◽  
pp. 5035 ◽  
Author(s):  
Yuan-sheng Ma ◽  
Xu Liu ◽  
Pei-fu Gu ◽  
Jin-fa Tang
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optical Constants ◽  
Artificial Neural

Synaptic plasticity, metaplasticity and memory effects in hybrid organic–inorganic bismuth-based materials

Nanoscale ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 1080-1090 ◽  
Author(s):  
Tomasz Mazur ◽  
Piotr Zawal ◽  
Konrad Szaciłowski
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Plasticity ◽  
Memory Effects ◽  
Bismuth Iodide ◽  
Artificial Neural ◽  
Thin Film Devices

Herein, we present memristive, thin film devices made of methylammonium bismuth iodide that exhibit a wide variety of neuromorphic effects simultaneously. Described materials have the potential to become universal cells in artificial neural networks.

scholarly journals Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 138 ◽  
Author(s):  
Syed Naqvi ◽  
Tallha Akram ◽  
Sajjad Haider ◽  
Muhammad Kamran ◽  
Aamir Shahzad ◽  
...  
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Critical Parameters ◽  
Superconducting Thin Film ◽  
Current Voltage ◽  
Wide Range ◽  
Artificial Neural

Contemplating the importance of studying current–voltage curves in superconductivity, it has been recently and rightly argued that their approximation, rather than incessant measurements, seems to be a more viable option. This especially becomes bona fide when the latter needs to be recorded for a wide range of critical parameters including temperature and magnetic field, thereby becoming a tedious monotonous procedure. Artificial neural networks have been recently put forth as one methodology for approximating these so-called electrical measurements for various geometries of antidots on a superconducting thin film. In this work, we demonstrate that the prediction accuracy, in terms of mean-squared error, achieved by artificial neural networks is rather constrained, and, due to their immense credence on randomly generated networks’ coefficients, they may result in vastly varying prediction accuracies for different geometries, experimental conditions, and their own tunable parameters. This inconsistency in prediction accuracies is resolved by controlling the uncertainty in networks’ initialization and coefficients’ generation by means of a novel entropy based genetic algorithm. The proposed method helps in achieving a substantial improvement and consistency in the prediction accuracy of current–voltage curves in comparison to existing works, and is amenable to various geometries of antidots, including rectangular, square, honeycomb, and kagome, on a superconducting thin film.

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