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Silicon testing results are regularly collected for a particular lot of wafers to study yield loss from test result diagnostics. Product engineers will analyze the diagnostic results and perform a number of physical failure analyses to detect systematic defects which cause yield loss for these sets of wafers in order to feedback the information to process engineers for process improvements. Most of time, the systematic defects that are detected are major issues or just one of the causes for the overall yield loss. This paper will present a working flow for using design analysis techniques combined with diagnostic methods to systematically transform silicon testing information into physical layout information. A new set of the testing results are received from a new lot of wafers for the same product. We can then correlate all the diagnostic results from different periods of time to check which blocks or nets have been highlighted or stop occurring on the failure reports in order to monitor process changes which impact the yield. The design characteristic analysis flow is also implemented to find 1) the block connections on a design that have failed electrical test or 2) frequently used cells that been highlighted multiple times.

Systematic yield detractors are normally expected to be identified by ATPG test result diagnostics. Different test patterns have been designed to test different functions. Test diagnostics can identify failed functions so that product engineers, based on testing results, can narrow down which block in the design performs this function. However, it is often hard to narrow down to a more specific region in a product. This paper will present a working flow for using design diffing techniques to extract layout structures and perform a geometry analysis flow combined with testing results to find most probable suspects that may cause noticeable yield loss.

Spectral beam combination expands the output power while keeps the beam quality of the combined beam almost the same as that of a single emitter. Spectral beam combination has been successfully achieved for high power fiber lasers, diode laser arrays and diode laser stacks. We have recently achieved the spectral beam combination of multiple single emitter diode lasers. Spatial beam combination and beam transformation are employed before beams from 25 single emitter diode lasers can be spectrally combined. An average output power about 220W, a spectral bandwidth less than 9 nm (95% energy), a beam quality similar to that of a single emitter and electro-optical conversion efficiency over 46% are achieved. In this paper, Rigorous Coupled Wave analysis is used to numerically evaluate the influence of emitter width, emitter pitch and focal length of transform lens on diffraction efficiency of the grating and spectral bandwidth. To assess the chance of catastrophic optical mirror damage (COMD), the optical power in the internal cavity of a free running emitter and the optical power in the grating external cavity of a wavelength locked emitter are theoretically analyzed. Advantages and disadvantages of spectral beam combination are concluded.

The detection of hazardous targets frequently requires a multispectral approach to image acquisition and analysis, which we have implemented in a software system called MATRE (multispectral automated target recognition and enhancement). MATRE provides capabilities of image enhancement, image database management, spectral signature extraction and visualization, statistical analysis of greyscale imagery, as well as 2D and 3D image processing operations. Our system is based upon a client-server architecture that is amenable to distributed implementation. In this paper, we discuss salient issues and requirements for multispectral recognition of hazardous targets, and show that our software fulfills or exceeds such requirements. MATRE's capabilities, as well as statistical and morphological analysis results, are exemplified with emphasis upon computational cost, ease of installation, and maintenance on various Unix platforms. Additionally, MATRE's image processing functions can be coded in vector-parallel form, for ease of implementation of SIMD-parallel processors. Our algorithms are expressed in terms of image algebra, a concise, rigorous notation that unifies linear and nonlinear mathematics in the image domain. An image algebra class library for the C + + language has been incorporated into the our system, which facilitates fast algorithm prototyping without the numerous drawbacks of descrete coding.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In Part I of this two-part series, we described the adaptive double-gated filter (DGF), a nonlinear CS detector that is sensitive to target shape and statistical properties. In particular, we derived the DGFs parameters from knowledge of target shape, size, and statistics, as well as background statistics. Several variants of the DGF were presented different for target recognition regimes (e.g., smooth targets against rough backgrounds, and vice versa). An additional variant of the DGF, known as the adaptive triple-gated-filter (TGF) was presented which aids in target registration. In this paper, we analyze the errors inherent in the DGF and TGF. Our analyses emphasize the performance of the DGF in limiting conditions, including high noise and poorly defined targets. Additionally, we discuss implementational optimization of the DGFs performance and time complexity. Examples of DGFs application to a database of field images presented, with discussion of results stated in terms of probability of hits and rate of false alarms.

Center-surround (CS) fields have long been identified in the human visual system as having properties of edge enhancement that facilitate the detection, location, and tracking of small objects. Unfortunately, the automatic digital implementation of such capabilities is not straightforward, since digital computers lack the neural functionality and connectivity inherent in the retina, optic chiasm, geniculate nuclei, and visual cortex. In this paper, we discuss efficient techniques for implementing CS detectors in terms of a class of nonlinear filters. In particular, we emphasize image enhancement schemes that cue the registration of CS detectors over the target, thus rendering our filters implementationally feasible in terms of hit rate and computational cost. Examples are given of the CS detectors that we call the adaptive Double- Gated and Triple-Gated filters (DGF and TGF). Such filters account for spatial nonstationary input. We further show how the DGF and TGF are useful for object detection in multispectral imagery. Analyses emphasize error and computational cost in practical sensing scenarios.