Showing papers by "Evelyn L. Hu published in 1986"
07 Apr 1986
TL;DR: A key result of this work is to have established sensitivity criteria for color detection in microelectronic structures as well as more easily and flexibly performing oxide thickness measurements in situ, than can be accomplished with standard techniques, such as ellipsometry.
Abstract: We present a new method of automated inspection of microelectronic structures. The method is based on color rather than black and white vision, and is a first such application of color vision to inspection for microelectronics fabrication. We describe the general method and demonstrate its practical implementation in the measurement of oxide thicknesses. A key result of this work is to have established sensitivity criteria for color detection in microelectronic structures. The resolution achieved allows us to measure differences in oxide thickness to approximately 30 Angstroms or better. By using the Ohta coordinates, our system can discrimimate between cyclically repeating hues. This determination can be done very rapidly (approximately 100 milliseconds) and does not require a complex (and thus expensive) computer system. An additional advantage of our method is the possibility of more easily and flexibly performing oxide thickness measurements in situ, than can be accomplished with standard techniques, such as ellipsometry.
20 citations
01 Apr 1986
TL;DR: It is demonstrated that once the essential structure of the physical model is correctly described, classical probability theory yields simple algorithms for the evaluation of the degree of evidence as it propagates through complex inference networks, including diagnostic trees and multicausal nets.
Abstract: We present a new method of inference applicable to robots and other intelligent machines. Inferences drawn by intelligent machines are based on measurements gathered through sensory perception. We demonstrate that the methods for managing uncertainty of meaning, which recently have been extended to a wide variety of non-human systems, generally yield qualitatively incorrect results when applied to the uncertainty of evidence available to an intelligent machine. We show that even in very simple machines, no amount of sophistication in the mathematical algorithms can compensate for incorrect assumptions about the physical model. Conversely, we also demonstrate that once the essential structure of the physical model is correctly described, classical probability theory yields simple algorithms for the evaluation of the degree of evidence as it propagates through complex inference networks, including diagnostic trees and multicausal nets. As a first application, we have derived the probability algorithms relevant to diagnosing the malfunctioning of a thermal evaporator. For this system, an inference network has been constructed and compared to an implementation based on a MYCIN-type expert system. The laboratory implementation of the system is also described.
3 citations