Neural network modeling of reactive ion etching using principal component analysis of optical emission spectroscopy data

IEEE International Symposium on Semiconductor Manufacturing Conference, Proceedings

Volumn , Issue , 2002, Pages 415-420

  Hong, Sang J ^a   May, Gary S ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Low k dielectrics; Neural networks; Optical emission spectroscopy; Principal component analysis; Reactive ion etching

Indexed keywords

ALGORITHMS; ANISOTROPY; BACKPROPAGATION; DATA ACQUISITION; DIELECTRIC MATERIALS; LIGHT EMISSION; MATHEMATICAL MODELS; NEURAL NETWORKS; POLYMERS; PRINCIPAL COMPONENT ANALYSIS; REACTIVE ION ETCHING; SPECTROSCOPIC ANALYSIS;

BENZOCYCLOBUTENE; DIELECTRIC POLYMER; ERROR BACK-PROPAGATION ALGORITHM; MULTI-WAY PRINCIPAL COMPONENT ANALYSIS; OPTICAL EMISSION SPECTROSOCOPY;

SEMICONDUCTOR DEVICE MANUFACTURE;

EID: 0036078557     PISSN: 1523553X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Statistical experimental design in plasma etching modeling
2. Advantages of plasma etch modeling using neural networks over statistical techniques
3. Application of optical emission spectroscopy to semiconductor device fabrication
4. Endpoint detection in plasma etching
5. Soft mask for via patterning in benzocyclobutene
6. Spatial characterization of wafer state using principal component analysis of optical emission spectra
7. Monitoring batch process using multi-way principal component analysis
8. Reactive ion etch modeling using neural networks and simulated annealing