This paper describes research into a non-contact system for the inspection of fine pitch electronic components on manufactured electronic assemblies. A far-field diffraction pattern from the leads of a tape automated bonding component is captured and stored on a frame store. The diffraction pattern is statistically represented and then classified using an artificial neural network. Results from simulation and experimentation show the feasibility of the technique.

This paper describes research into a non-contact system for the measurement of components and solder on printed circuit boards (PCBs). The objective of the system is the inspection of the PCB to identify faults in the manufacturing process. Two approaches are described: diffraction pattern analysis and fringe pattern analysis. For the diffraction pattern analysis, a diffraction pattern is formed by the lead array on a component. For fringe pattern analysis, a fringe pattern is generated on the PCB using either interferometric methods or projection of a grating. For both methods, an image of the pattern is captured and stored on a frame store. The pattern is then processed to provide data from which parameters which relate to solder joint integrity can be determined. Results demonstrate the feasibility of the methods.

A technique for the quality assessment of fine-pitch electronic components is described. A system for the capture of Fraunhofer diffraction patterns reflected from the component is presented. The processing of the image to enable classification by a neural network is discussed. Simulation results are presented, showing the feasibility of the technique.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper describes research into a noncontact system for the measurement of components and solder on printed circuit boards (PCBs). The objective of the system is the inspection of the PCB to identify faults in the manufacturing process. A fringe pattern is generated on the PCB, using either interferometric methods or projection of a grating, and the image of the fringe pattern is captured and stored on a frame store. The pattern is then processed to reconstruct the 3D shape of the PCB surface from which parameters which relate to PCB integrity can be determined. Results demonstrate the feasibility of the method.

The feasibility of using diffraction pattern analysis for the quality assessment of small components, with a specific application aimed at electronic components, is discussed. An electro-optical system for the capture of reflected diffraction patterns is presented. Preliminary simulation results for simple faults have been obtained and are used to illustrate the described feature vector and neural network classifier.