Jigsaw three-dimensional (3D) imaging laser radar is a compact, light-weight system for imaging highly obscured targets through dense foliage semi-autonomously from an unmanned aircraft. The Jigsaw system uses a gimbaled sensor operating in a spot light mode to laser illuminate a cued target, and autonomously capture and produce the 3D image of hidden targets under trees at high 3D voxel resolution. With our MIT Lincoln Laboratory team members, the sensor system has been integrated into a geo-referenced 12-inch gimbal, and used in airborne data collections from a UH-1 manned helicopter, which served as a surrogate platform for the purpose of data collection and system validation. In this paper, we discuss the results from the ground integration and testing of the system, and the results from UH-1 flight data collections. We also discuss the performance results of the system obtained using ladar calibration targets.

We have developed high-speed ladar read out integrated circuits (ROIC) and high performance detector arrays for high-resolution 3D range imaging applications. Our focal plane array imagers are based on direct detection ladar architecture that provides the 3D shape of objects in a single laser pulse. We have demonstrated a 64/spl times/64 element, 100-/spl mu/m pixel pitch CMOS ROIC having low noise (40e-) and high analog bandwidth (115 MHz) for short pulse laser signal detection at long ranges. The read out chip allows in-pixel range thresholding and capturing of the time gated intensity profiles of the ladar echoes. The readout can accommodate either p-i-n diode or avalanche photodiode (APD) detector arrays operating in the visible through IR spectral bands. We discuss the status of recent detector and focal plane array developments for the SWIR, MWIR, and LWIR bands. We will also review our recent field test results of the SWIR 64/spl times/64 element focal plane array demonstrating the capability of discriminating object separations of about 0.5-m.

We have developed a three-dimensional (3D) imaging ladar focal plane array (FPA) for military and commercial applications. The FPA provides snap-shot, direct detection, high-resolution range and range-sampled intensity imaging capability on a single chip. The FPA is made of a 64x64 element, 100-μm pixel pitch detector array that is directly bump bonded to a matched CMOS based silicon readout integrated circuit (ROIC) with parallel ladar signal processing at each pixel. A room temperature, SWIR InGaAs detector variant for imaging near 1.5-μm wavelengths and a cooled MWIR HgCdTe detector variant for imaging near 3-μm to 5-μm wavelengths have been fabricated. We have built a prototype SWIR FPA, integrated it to a compact, transportable SWIR flash ladar transceiver, and collected initial range images outdoors. We present the measured performances of the detector, the readout, and the image data collected with the focal plane array.

We are developing a novel 2-dimensional focal plane array (FPA) with read-out integrated circuit (ROIL) on a single chip for 3 dimensional (3-D) laser radar (ladar) imaging. The ladar will provide high-resolution range and range-resolved intensity images for detection and identification of difficult targets. The initial full imaging-camera-on-a-chip system will be a 64x64 element, 100-mum pixel-size detector array that is directly bump bonded to a low-noise (30 electrons) 64x64 array silicon CMOS-based ROIC. The architecture is scalable to 256x256 or higher arrays depending on the system application. The system will provide all the required electronic processing at pixel level and the smart FPA enables directly producing the three-dimensional (angle-angle-range) or four-dimensional (angle-angle-range-intensity) format data to be captured with a single laser pulse. The detector arrays are made of uncooled InGaAs PIN device for SWIR imaging at 1.5 mum wavelength and cooled HgCdTe PIN device for MWIR imaging at 3.8 mum wavelength. We are also investigating concepts using multicolor detector arrays for simultaneous imaging at multiple wavelengths (active-active and active-passive) that would provide additional spectral dimension capability for enhanced detection and identification of deep-hide targets. The system is suited for flash ladar imaging, for combat identification of ground targets from airborne platforms, flash-ladar imaging seekers, and autonomous robotic/automotive vehicle navigation and collision avoidance applications.

Rapid yield ramp up in wafer processing fabs has become the most important task for process and yield engineers. In-line process monitoring using inspection tools are the traditional approaches to monitor excursion within the process line. Minor gains in yield ca increase revenue per wafer significantly, hence defect detection and defect feedback into process line is becoming the corner stone of fab yield success. Number of mask levels are becoming important to process fabs. Monitoring these mask levels so as to detect defects and feed information to the process line has become crucial to process yield. In order for this to happen a good understanding of the defects caused at the mask levels is important. An attempt will be made to discus the various types of defects present in the photomask levels and their relationships to their in-line detection. The discussion deals with scum defects, isolated bridging defects, pattern aspect ratio, and isolated and dense region patterning issues.

A statistical spectral band selection procedure and classifiers for an active multispectral laser radar (LADAR) sensor are described. The sensor will operate in the 1 to 5 mu m wavelength region. The algorithms proposed are tested using library reflectance spectra for some representative background materials. The material classes considered include both natural (vegetation and soil) and man-made (camouflage cloth and tar-asphalt). The analysis includes noise statistics due to Gaussian receiver noise and target induced speckle variations in the LADAR return signal intensity. The results of this analysis are then directly applied to an artificially generated spatial template of a scene consisting of these four material classes. The performance of four different classifier algorithms, which include a minimum distance classifier, a log-domain minimum distance classifier, a Bayes speckle-only classifier, and a Bayes speckle-Gaussian classifier, are evaluated. We show that the Bayesian classifier designed for speckle and Gaussian noise statistics outperforms the other classifiers. Our results also indicate that even when exact knowledge of the observation model is available, the classifier performance for speckled images can be poor unless the number of integrated speckle cells is large. (C) 1998 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(98)00403-6].

We describe a tunable, 1.3 to 5 mu m wavelength reflectance measurement system using an optical parametric oscillator (OPO) as the light source. The OPO source consists of a I mu m Nd:YAG laser which is frequency shifted to 1.3-5 mu m wavelengths using a periodically poled lithium niobate (PPLN) nonlinear optical crystal. The system design, calibration, and measurement of the directional-hemispherical reflectance (DHR) factor and the bi-directional reflectance distribution function (BRDF) of different target materials are presented.