A Type of Delay Feedback Control of Chaotic Dynamics in a Chaotic Neural Network

A chaotic neural network consisting of chaotic neurons exhibits such rich dynamical behaviors as nonperiodic associative memory. But it is difficult to distinguish the stored patterns from others, since the chaotic neural network shows chaotic wandering around the stored patterns. In order to apply the nonperiodic associative memory to information search or pattern identification, it is necessary to control chaotic dynamics. In this paper, we propose a delay feedback control method for the chaotic neural network. Computer simulation shows that, by means of the control method, the chaotic dynamics in the chaotic neural network are changed. The output sequence of the controlled network wanders around one stored pattern and its reverse pattern

Selective area epitaxy of ultra-high density InGaN quantum dots by diblock copolymer lithography

Highly uniform InGaN-based quantum dots (QDs) grown on a nanopatterned dielectric layer defined by self-assembled diblock copolymer were performed by metal-organic chemical vapor deposition. The cylindrical-shaped nanopatterns were created on SiNx layers deposited on a GaN template, which provided the nanopatterning for the epitaxy of ultra-high density QD with uniform size and distribution. Scanning electron microscopy and atomic force microscopy measurements were conducted to investigate the QDs morphology. The InGaN/GaN QDs with density up to 8 × 1010 cm-2 are realized, which represents ultra-high dot density for highly uniform and well-controlled, nitride-based QDs, with QD diameter of approximately 22-25 nm. The photoluminescence (PL) studies indicated the importance of NH3 annealing and GaN spacer layer growth for improving the PL intensity of the SiNx-treated GaN surface, to achieve high optical-quality QDs applicable for photonics devices