Grouping Method Of Image Fragments Of Adjacent Dislocation Etch Pits Of The Semiconductor Wafer

An increase in production volumes of gallium arsenide semiconductor devices determines the need for better control of dislocations of semiconductor wafer.The grouping method of image fragments of adjacent dislocation etch pits of the semiconductor wafer is proposed in the article. Adjacent fragments will be allocated in the pre-binarized image of wafer surface, which contains adjacent fragments of etch pits of dislocation loops after treatment by the described method. Improved methods for determining the loop line width determines the edge line width of etch pits of suspected dislocations, given the variability of their display in the binarized image. The current loop line width is compared to the reference line width of the dislocation loop.The grouping method of image fragments of adjacent dislocation etch pits of the semiconductor wafer defines recovery of loop lines branching, takes into account various options of line adjacency and determines the direction of further recovery of loop line of dislocation etch pits. A step by step description of the method is given

Photonic reservoir computing: a new approach to optical information processing

Despite ever increasing computational power, recognition and classification problems remain challenging to solve. Recently advances have been made by the introduction of the new concept of reservoir computing. This is a methodology coming from the field of machine learning and neural networks and has been successfully used in several pattern classification problems, like speech and image recognition. The implementations have so far been in software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware implementation of this concept because of its inherent parallelism and unique nonlinear behaviour. We propose using a network of coupled Semiconductor Optical Amplifiers (SOA) and show in simulation that it could be used as a reservoir by comparing it on a benchmark speech recognition task to conventional software implementations. In spite of several differences, they perform as good as or better than conventional implementations. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. We will also address the role phase plays on the reservoir performance

Quantum Information Processing with Ferroelectrically Coupled Quantum Dots

I describe a proposal to construct a quantum information processor using ferroelectrically coupled Ge/Si quantum dots. The spin of single electrons form the fundamental qubits. Small (<10 nm diameter) Ge quantum dots are optically excited to create spin polarized electrons in Si. The static polarization of an epitaxial ferroelectric thin film confines electrons laterally in the semiconductor; spin interactions between nearest neighbor electrons are mediated by the nonlinear process of optical rectification. Single qubit operations are achieved through "g-factor engineering" in the Ge/Si structures; spin-spin interactions occur through Heisenberg exchange, controlled by ferroelectric gates. A method for reading out the final state, while required for quantum computing, is not described; electronic approaches involving single electron transistors may prove fruitful in satisfying this requirement.Comment: 10 pages, 3 figure