Abstract
The paper describes a unique approach to optoelectronic elements application in artificial intelligence. Previously we considered molecular neural networks on the base of the functional porous silica thin films. But, for the successful molecular neural network design, we need efficient connections among them. Therefore we are presenting a material with tuneable non-linear optical (NLO) properties to be used for the optical signal transfer. The idea is briefly described and then followed by an experimental part to validate its feasibility. Promising results show that it is possible to design and synthesize the material with tuneable NLO properties.
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Financial support for this investigation has been provided by the National Centre of Science (Grant-No: 2015/17/N/ST5/03328).
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Laskowska, M., Laskowski, Ł., Jelonkiewicz, J., Piech, H., Filutowicz, Z. (2018). Porous Silica-Based Optoelectronic Elements as Interconnection Weights in Molecular Neural Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_13
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