  The SEMATECH sponsored J-88-E project teaming Texas Instruments with
NeuroDyne (et al.) focused on Fault Detection and Classification (FDC) on a Lam
9600 aluminum plasma etch reactor, used in the process of semiconductor
fabrication. Fault classification was accomplished by implementing a series of
virtual sensor models which used data from real sensors (Lam Station sensors,
Optical Emission Spectroscopy, and RF Monitoring) to predict recipe setpoints
and wafer state characteristics. Fault detection and classification were
performed by comparing predicted recipe and wafer state values with expected
values. Models utilized include linear PLS, Polynomial PLS, and Neural Network
PLS. Prediction of recipe setpoints based upon sensor data provides a
capability for cross-checking that the machine is maintaining the desired
setpoints. Wafer state characteristics such as Line Width Reduction and
Remaining Oxide were estimated on-line using these same process sensors (Lam,
OES, RFM). Wafer-to-wafer measurement of these characteristics in a production
setting (where typically this information may be only sparsely available, if at
all, after batch processing runs with numerous wafers have been completed)
would provide important information to the operator that the process is or is
not producing wafers within acceptable bounds of product quality. Production
yield is increased, and correspondingly per unit cost is reduced, by providing
the operator with the opportunity to adjust the process or machine before
etching more wafers.
