Title :
A neural network based approach for surveillance and diagnosis of statistical parameters in IC manufacturing process
Author :
Zhang, W. ; Milor, L.
Author_Institution :
Dept. of Electr. Eng., Maryland Univ., College Park, MD, USA
Abstract :
Presents a new approach for monitoring and diagnosing potential faults in the IC manufacturing process. A backpropagation neural network based diagnosing model is employed to synthesize the complicated mapping from process measurements to the unmeasurable process disturbances. This model is trained to detect significant shifts of the disturbances. Due to the inverse mapping diagnosis becomes very efficient and is quite promising for real time applications. Several mathematical issues involved in this approach and an illustrative example are discussed.
Keywords :
backpropagation; integrated circuit manufacture; neural nets; production engineering computing; production testing; IC manufacturing process; backpropagation neural network; complicated mapping; diagnosis; neural network based approach; statistical parameters; surveillance; unmeasurable process disturbances; Circuit faults; Condition monitoring; Density measurement; Fabrication; Fluctuations; Integrated circuit yield; Intelligent networks; Manufacturing processes; Neural networks; Surveillance;
Conference_Titel :
Semiconductor Manufacturing Science Symposium, 1993. ISMSS 1993., IEEE/SEMI International
Conference_Location :
San Francisco, CA, USA
Print_ISBN :
0-7803-1212-0
DOI :
10.1109/ISMSS.1993.263688
