Application of Artificial Intelligence for Optimization of Organic Solar Cells Production Process

Grazia Lo Sciuto

Abstract


The study of organic solar cells (OSCs) has been rapidly developed in recent years. Organic solar cell technology is sought after mainly due to the ease of manufacture and their exclusive properties such as mechanical flexibility, light-weight, and transparency. These properties of OSCs are well-suited for unconventional applications with power conversion efficiencies more high than 10%. The flexibility of the used substrates and the thinness of the devices make OSCs ideal for roll-to-roll production. However the organic solar cells still have very low conversion efficiencies due to degradation and stability of the technology. In order to extract their full potential, OSCs have to be optimized. On the other hand the production chain of the organic solar cells (OSC) can take advantage of the use of artificial intelligence (AI). In fact the integration into the production workflow makes solar cells more competitive and efficient. This paper presents some applications of the AI for optimization of OSCs production processes

Full Text: PDF

References
  1. Lo Sciuto, G., Capizzi, G., Coco, S., Shikler, R., "Geometric shape optimization of organic solar cells for efficiency enhancement by neural networks." (2017) Lecture Notes in Mechanical Engineering, pp. 789-796. CrossRef
  2. Barnea, S.N., Lo Sciuto, G., Hai, N., Shikler, R., Capizzi, G., Wozniak, M., Polap, D., "Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network." (2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, pp. 378-389. CrossRef
  3. Ye, L.; Hu, H.; Ghasemi, M.; Wang, T.; Collins, B.A.; Kim, J.H.; Jiang, K.; Carpenter, J.H.; Li, H.; Li, Z.; et al. "Quantitative relations between interaction parameter, miscibility and function in organic solar cells." Nat. Mater. 2018, 17, 253-260. CrossRef
  4. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610-621 (1973) CrossRef
  5. Capizzi, G., Sciuto, G.L., Napoli, C., Tramontana, E., Wozniak, M.: Automatic classification of fruit defects based on co-occurrence matrix and neural networks. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 861-867, September 2015. CrossRef

Full Text:

PDF

We use cookies that are necessary for the website to function and cannot be switched off in our systems. Click here for more information.


Photonics Letters of Poland - A Publication of the Photonics Society of Poland
Published in cooperation with SPIE

ISSN: 2080-2242