X-Ray Diffraction Method to Detect Defects in Cubic Semiconductor (100) Wafers

NASAs software technology uses an Image Quality Indicator (IQI)-based model that can predict whether cracks of a certain size can be detected, as well as a model that can provide appropriate conditions to optimize x-ray crack detection setup. Because this modeling software can predict minimum crack sizes that can be detected by a particular X-ray radiography testing setup, users can test various setups until the desired crack detection capabilities are achieved (predicted) by the modeling system. These flaw size parameter models use a set of measured inputs, including thickness sensitivity, detector modulation transfer function, detector signal response function, and other setup geometry parameters, to predict the minimum crack sizes detectable by the testing setup and X-ray angle limits for detecting such flaws. Current X-ray methods provide adequate control for detection of volumetric flaws but do not provide a high probability of detection (POD), and crack detection sensitivity cannot be verified for reliable detection. This results in reduced confidence in terms of crack detection. Given that these cracks, if undetected, can cause catastrophic failure in various systems (e.g., pressure vessels, etc.), verifying that X-ray radiography systems used for NDE can detect such cracks is of the utmost importance in many applications.

Additive manufacturing or 3-D printing is a rapidly growing field where solid, objects can be produced layer by layer. This technology will have a significant impact in many areas including industrial manufacturing, medical, architecture, aerospace, and automotive. The advantages of additive manufacturing are reduction in material costs due to near net shape part builds, minimal machining required, computer assisted builds for rapid prototyping, and mass production capability. Traditional thermal nondestructive evaluation (NDE) techniques typically use a stationary heat source such as flash or quartz lamp heating to induce a temperature rise. The defects such as cracks, delamination damage, or voids block the heat flow and therefore cause a change in the transient heat flow response. There are drawbacks to these methods.

The Pyramid Image Quality Indicator is based on the shape of a tall, 4-sided pyramid. Each side of the pyramid has a pair of vertical trenches which draw closer to each other and get narrower as they approach the tip of the pyramid. Inside the pyramid is a hollowed out conical section which may contain internal features for determining resolution or inserts that can be used for measuring contrast sensitivity. The system can be economically 3D printed and then coated, if need be, with high x-ray absorbing material. When a CT system operator is scanning a part, a specific method for that part which might include a large number of variables such as x-ray voltage, detector-to-source spacing, pixel size, etc. This established method will result in an effective level of detail for the resulting scan. The IQI is used to measure that level of detail. The operator may follow up the scan of the part with an identical scan of the IQI, which will allow a realistic measurement of parameters, like effective resolution or contrast sensitivity. The interior of the Pyramid IQI uses a 3D variant suited for 3D CT scan data tools, known as penetrameters. These penetrameters are a solid disc of material. A stack of discs of different diameters would accommodate a range of different thicknesses. The Pyramid IQI can be easily scaled for either larger or smaller parts. It can also be 3D printed using either plastic or metal additive manufacturing. This allows an end-user to match the material density of the IQI to that of the actual part.

NASA's System for In-situ Defect (e.g., porosity, fiber waviness) Detection in Composites During Cure consists of an ultrasonic portable automated C-Scan system with an attached ultrasonic contact probe. This scanner is placed inside of an insulated vessel that protects the temperature-sensitive components of the scanner. A liquid nitrogen cooling systems keeps the interior of the vessel below 38°C. A motorized X-Y raster scanner is mounted inside an unsealed cooling container made of porous insulation boards with a cantilever scanning arm protruding out of the cooling container through a slot. The cooling container that houses the X-Y raster scanner is periodically cooled using a liquid nitrogen (LN2) delivery system. Flexible bellows in the slot opening of the box minimize heat transfer between the box and the external autoclave environment. The box and scanning arm are located on a precision cast tool plate. A thin layer of ultrasonic couplant is placed between the transducer and the tool plate. The composite parts are vacuum bagged on the other side of the tool plate and inspected. The scanning system inside of the vessel is connected to the controller outside of the autoclave. The system can provide A-scan, B-scan, and C-scan images of the composite panel at multiple times during the cure process. The in-situ system provides higher resolution data to find, characterize, and track defects during cure better than other cure monitoring techniques. In addition, this system also shows the through-thickness location of any composite manufacturing defects during cure with real-time localization and tracking. This has been demonstrated for both intentionally introduced porosity (i.e., trapped during layup) as well processing induced porosity (e.g., resulting from uneven pressure distribution on a part). The technology can be used as a non-destructive evaluation system when making composite parts in in an oven or an autoclave, including thermosets, thermoplastics, composite laminates, high-temperature resins, and ceramics.

The in situ inspection technology for additive manufacturing combines different types of cameras strategically placed around the part to monitor its properties during construction. The IR cameras collect accurate temperature data to validate thermal math models, while the visual cameras obtain highly detailed data at the exact location of the laser to build accurate, as-built geometric models. Furthermore, certain adopted techniques (e.g., single to grouped pixels comparison to avoid bad/biased pixels) reduce false positive readings. NASA has developed and tested prototypes in both laser-sintered plastic and metal processes. The technology detected errors due to stray powder sparking and material layer lifts. Furthermore, the technology has the potential to detect anomalies in the property profile that are caused by errors due to stress, power density issues, incomplete melting, voids, incomplete fill, and layer lift-up. Three-dimensional models of the printed parts were reconstructed using only the collected data, which demonstrates the success and potential of the technology to provide a deeper understanding of the laser-metal interactions. By monitoring the print, layer by layer, in real-time, users can pause the process and make corrections to the build as needed, reducing material, energy, and time wasted in nonconforming parts.