Abstract: A system for X-ray topography, the system includes a source assembly, a detector assembly, a filter and a processor. The source assembly is configured to direct at least an X-ray beam to impinge, at an angle, on a first surface of a sample, the X-ray beam is divergent when impinging on the first surface. The detector assembly is configured to detect the X-ray beam that had entered the sample at the first surface, diffracted while passing through the sample and exited the sample at a second surface that is opposite to the first surface, and to produce an electrical signal in response to the detected X-ray beam. The filter is mounted between the source assembly and the first surface, and is configured to attenuate an intensity of a selected spectral portion of the X-ray beam. The processor is configured to detect one or more defects in the sample based on the electrical signal.

Abstract: A system for X-ray topography, the system includes a source assembly, a detector assembly, a filter and a processor. The source assembly is configured to direct at least an X-ray beam to impinge, at an angle, on a first surface of a sample, the X-ray beam is divergent when impinging on the first surface. The detector assembly is configured to detect the X-ray beam that had entered the sample at the first surface, diffracted while passing through the sample and exited the sample at a second surface that is opposite to the first surface, and to produce an electrical signal in response to the detected X-ray beam. The filter is mounted between the source assembly and the first surface, and is configured to attenuate an intensity of a selected spectral portion of the X-ray beam. The processor is configured to detect one or more defects in the sample based on the electrical signal.

Abstract: Evolutionary algorithms are used to find a global solution to the fitting of experimental X-ray scattering data to simulated models. A recombination operator combines two or more parameter vectors from one iteration of simulated scattering data to form a new parameter vector for the next iteration, in a manner such that there is a high probability that the new parameter will better fit the experimental data than any of the parent parameters. A mutation operator perturbs the value of a parent vector, to permit new regions of the error function to be examined, and thereby avoid settling on local minima. The natural selection guarantees that the parameter vectors with the best fitness will be propagated into future iterations.