SiGlaz Intelligent Defect Analyzer (IDA) software
automatically analyzes wafer defect maps and identifies defect
signatures resulting from equipment failures and process excursions.
IDA provides several algorithms for recognizing spatial signatures,
including pattern matching, zonal analysis, repeater analysis
and object analysis. When the defect signature is characterized
by a distinctive shape, it is usually well-suited for object signature
analysis.
IDA software defines an object as a group of
defects that lie within a defined spatial proximity. Each object
may be characterized by attributes that describe its size, shape,
orientation and location. An object signature is an object whose
attributes match a set of rules defined by the user.
Semiconductor fabs currently use defect count
or defect density as a triggering mechanism for their Statistical
Process Control. However, in the early stages of a process excursion,
a careful analysis of the wafer inspection data may exhibit a
defect signature, but the defect count or defect density may be
too low to trigger the SPC. In this case, the process excursion
will go undetected until the subsequent inspection cycle. When
the SPC threshold is finally triggered, analysis of the root cause
can often take several additional hours. These delays in identifying
the root cause put a considerable number of product wafers at
risk.
Manual review of all inspection data could potentially
identify these low defect count signatures, but in addition to
being highly subjective, manual review is both expensive and time-consuming.
Defect engineers and fab managers are seeking a way to automate
the review process, thereby eliminating subjectivity and operator
error from the review process, and accelerating the root cause
analysis.
SiGlaz Intelligent Defect Analysis (IDATM) software
provides fabs with an automatic excursion monitoring capability
that integrates seamlessly into existing yield management architecture
to provide immediate value-added functionality. Signature recognition
and root cause analysis of a process excursion can now be accomplished
in seconds.
Please refer to White
Paper for Pdf file of this page.
INTELLIGENT
DEFECT ANALYSIS,
Framework For Integrated Data Management
Spatial signature analysis (SSA) is one of the
key technologies that semiconductor manufacturers will begin to
deploy into their manufacturing processes in order to improve
yield learning. In order to perform rapid root cause analysis
of process excursions the defect signature information derived
from SSA must be integrated with other data bases in the fab.
However, some of the fundamental impediments to integrated data
management identified in the 2003 Sematech International Technology
Roadmap for Semiconductors (ITRS) are a lack of standards on which
to base system communications, standard data formats, and a common
software interface between data depositories. “The ability
to automate the retrieval of data from a variety of database sosurces,
such as based on statistical process control charts and other
system cues will be required to efficiently reduce these data
sources to process-related information in a timely manner. To
close the loop on defect and fault sourcing capabilities, methods
must be established for integrating workflow information (such
as WIP data) with the DMS, particularly in commercial DMS systems.”
SiGlaz has introduced a
spatial signature analysis product called Intelligent Defect Analysis
(IDA) that automatically assimilates manufacturing process data
collected from inspection equipment and other fab databases to
determine the root-cause of a process excursion. IDA incorporates
an advanced system framework that facilitates communication between
dissimilar databases and moves beyond the operator-driven paradigm
that is currently used in the fab to an event-driven paradigm
that is emerging in advanced process control systems. SiGlaz uses
artificial intelligence methods that combine both spatial and
temporal elements in its signature analysis. The method deploys
a teaching algorithm and data mining to emulate the domain expert
in recognizing anomalies occurring during the wafer manufacturing
process. This paper will describe both the architecture and components
of this automated process control technique.
