Optical neural network architecture in photoferroelectrics

Babur Y., Mamedov A.

FERROELECTRICS, cilt.296, ss.83-89, 2003 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 296
  • Basım Tarihi: 2003
  • Doi Numarası: 10.1080/00150190390239161
  • Sayfa Sayıları: ss.83-89


We present a novel, versatile optoelectronic neural network architecture for implementing supervised learning in photo-ferroelectrics (SrxBa1-xNb2O6, Bi12XO20; X=Ge, Si, Ti, LiNbO3: Fe, LiTaO3:Fe and LiTaO3:Cr). The system is based on spatial multiplexing rather than the more commonly used angular multiplexing of interconnect gratings. This sample, single-crystal architecture implements a variety of multi-propagations and Marr-Albus-Kanerva style algorithms. Extensive simulations show how suitable modified supervised learning algorithms compensate for beam depletion, re-scattering, absorption and decay effects of the crystals. This type of implementation also benefits strongly from recently discovered tunable stability phenomena in the read-write properties of anisotropic crystals like photo-ferroelectric.