Preprint Article Version 1 This version is not peer-reviewed

Optimizing Organic Solar Cells Manufacturing Process by Means of AFM Measurements and Neural Networks

Version 1 : Received: 22 March 2018 / Approved: 23 March 2018 / Online: 23 March 2018 (04:41:23 CET)

How to cite: Capizzi, G.; Lo Sciuto, G.; Napoli, C.; Shikler, R.; Wozniak, M. Optimizing Organic Solar Cells Manufacturing Process by Means of AFM Measurements and Neural Networks. Preprints 2018, 2018030194 (doi: 10.20944/preprints201803.0194.v1). Capizzi, G.; Lo Sciuto, G.; Napoli, C.; Shikler, R.; Wozniak, M. Optimizing Organic Solar Cells Manufacturing Process by Means of AFM Measurements and Neural Networks. Preprints 2018, 2018030194 (doi: 10.20944/preprints201803.0194.v1).

Abstract

In this paper we devise a neural network based model to improve the production workflow of organic solar cells. The investigated neural model is used to reckon the relation between the organic solar cell's generated power and several device's properties like the geometrical parameters and the active layers thicknesses. Such measurements have been collected during an experimental campaign conducted on 80 devices. The collected data suggest that the maximum generated power depends on the active layer thickness. The mathematical model of such a relation has been determined by using a feedforward neural network architecture as universal function approximator. The performed simulations show a good agreement between simulated and experimental data with an overall error of about 9%. The obtained results demonstrate that the use of a neural model can be usefull to improve the organic solar cells manufacturing processes.

Subject Areas

nanotechnologies; photonics; nanoplasmonics; neural networks

Readers' Comments and Ratings (1)

Importance: How significant is the paper to the field?
Outstanding/highlight paper
0%
Significant contribution
100%
Incremental contribution
0%
No contribution
0%
Soundness of evidence/arguments presented:
Conclusions well supported
100%
Most conclusions supported (minor revision needed)
0%
Incomplete evidence (major revision needed)
0%
Hypothesis, unsupported conclusions, or proof-of-principle
0%
Comment 1
Received: 13 April 2018
The commenter has declared there is no conflict of interests.
Comment: The article deals with interesting issues related to production workflow of organic solar cells. The use of artificial neural networks is an innovative approach and gives satisfactory results.
+ Respond to this comment
Leave a public comment
Send a private comment to the author(s)
Rate this article
Views 0
Downloads 0
Comments 1
Metrics 0
Leave a public comment

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.