Download SAS Database Management System Used for Semiconductor Defect Learning

Posters
SAS® DATABASE MANAGEMENT SYSTEMS USED
FOR SEMICONDUCTOR DEFECT LEARNING
Tracy L Lord and Ken A. Wiggin
IBM MicroelectronicS Division
With increasing requirements for more complex
analysis, a structured database management system
is essential. This paper describes some of the
structures as well as the advantages and
disadvantages of the file systems used at IBM's
semiconductor production facility in Essex Junction,
Vermont.
optical deviations from these patterns.
The
manufacturing (MFG) group then uses scanning
electron microscopes (SEM) to inspect these
deviations, with the resulting information stored in a
database where process engineers can monitor defect
levels at their sectors via on-line statistical software.
Decreasing yield trends are highlighted by the
engineers at weekly meetings, who are then
responsible for action plans.
Introduction
The Database Structure:
There are many techniques for monitoring
reliability-related defects in a semiconductor process
line. These techniques include process-limited yields
(PLY), test process-limited yields (TPly), and failure
analysis (FA) of bum-in (BI), stress, and test fallout.
Of the many techniques used to screen for such
defects, process monitors, foreign material (FM)
checks, and maverick waferllot screens at inIinelwafer/moduleJstress test have lead to significant
improvements in the reliability of IBM's CMOS 2 and
2S products.
With the requirements for more complex analysis
growing exponentially, a structured· database
management system is essential. Process data,
wafer final test (WFT), BI data, module final test
(MFT), and in-line test (IlT) data are collected and
stored for short-and-Iong term retrieval. Different
filing structures are used by different persons with
varying results. This paper describes some of these
structures as well as their advantages and
disadvantages.
PLY data are stored as lot summaries in a SAS
database that has multiple members. The members
are divided into process sectors and contain data
collected at these sectors. The size of the database
is controlled by storing only lOT summary data and
not raw chip data. Data traffic is minimized by using
separate members within the parent data set, which
allows engineers to access data simultaneously.
Updates are provided daily during early morning
hours (usually at 2 am), which eliminates contention
among those users that need test data.
Abstract
Process United Yield
Samples of production hardware are selected for
visual inspection (VI) at strategic processes using
pattern recognition tools. The VI tools (Le., TENCOR
and KLA) are loaded with image patterns present on
the wafers at that process gate. The tool highlights
Test Process Umited Yield
Static random access memory (SRAM) products
are functionally tested post M2 level (Le., second
level of metal wiring). Three to seven wafers from
each SRAM lot are tested while two are selected for
full analysis. These two wafers are bit-fail-mapped
(BFM'd), after which FA delayers all failing chips until
the reason for failure is detected. This information is
stored in a database where process engineers
monitor the defect levels for their sectors via on-line
statistical software. Since SRAM's move continuously
through the line, defect monitors are positioned
throughout the fabrication process, with data timelined to key process dates, correlated with process or
tool problems, and used in other in-line monitoring
processes.
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Posters
Database StNcture:
The Database StIUCtUte:
The
TPLY data is stored in only one SAS database as
lot summaries. There are no multiple members
because the data is compiled by lot number, not
process sector. The database is updated as data
becomes available, which can be either daily or
hourly, depending on the cycle time of the analysis
group. The size of the database is minimized by
storing only lot summary data. Data traffic can be a
problem because the database is used quite
frequently for updates. Because this has become a
Significant problem for customers, database owners
have suggested breaking data into sectors.
Unfortunately, many analysis tasks require data to be
centrally located and this has hindered us from
segregating the data.
ILT and WFT data are stored as wafer and lot
summaries, with multiple SAS databases designed for
each product type. There are separate members for
wafer and lot data. Updates are completed as data
is sent from the tester, which can be daily or hourly
depending on the work in process (WI P) at test. The
size of the database is minimized by storing wafer
and lot summary data and not raw chip data. Data
traffic can be a problem when users submit queries
while the database is being updated.
While
database owners would like to break data into
sectors, they are not able to because sector data is
not always available during the updating procedure.
Maverick Screens at Test
Experiments are being conducted to analyze the
wafer final test characteristics of BI fails. SRAM
products use a backside laser scribe process whereby
a lot number, wafer number, and X-Y chip location is
written on the backside of every yielding chip. This
information, collected from samples of failing chips at
BI, is placed into a database which is then merged
with other in-line test and WFT databases to
determine additional cut pOints for screening out
reliability fails before the parts undergo BI.
Electrical, physical, and process analysis of
failures from field, system, card, module and wafer
stressitest have electrical parameters and device
process defects which impact reliability, outgoing
quality, and in-house yield. For those parameters and
defects monitored at in-line electrical kerf test
(structures placed in the scribe and dicing channels
between chips on a wafer), statistical maverick limits
are imposed to scrap wafers that have a Significantly
higher than normal probability of containing reliability,
outgoing quality, or excessive yield loss problems.
Maverick screens are calculated using a statistical
screen program written in SAS. This program uses
assumptions of a binomial distribution for attribute
(defect) data and a normal distribution for variable
data. Statistical maverick limits are calculated using
an iterating process of calculating a maverick limit,
removing product that fails this limit from product
distribution, recalculating a maverick limit with the
revised distribution, and removing product until none
exceeds the maverick limit. This final calculation
becomes the maverick limit. All calculations use an
arbitrary probability of acceptance of 0.999. The
resulting maverick limits (with a bUSiness modifier for
cost effectiveness) are then imposed on current
production and revised periodically as learning occurs.
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Laser ScrIbe Experiments
The Database StnIcture:
Raw chip data was stored for this experiment in
one SAS database. Multiple members were not
present because this was a one-time experiment.
Data were updated after being uploaded from the
tester. This can occur daily or hourly depending on
when experimental lots are tested.. Because the
database is huge, data traffic can be a problem for
customers when queries are made while the database
is updating. Since this was a one-time experiment,
database owners made all data available to everyone
once the experiment was complete.
Posters
Conclusions
Techniques using process-limited yields, test
process-limited yields, and failure analysis of burn-in,
stress, and test fallout have resulted in an impressive
improvement in the outgoing quality of our
semiconductors products. This can be illustrated by
our MFT yields, BI stress yields, and in the results of
our reliability stress monitoring. The key to these
improvements is the use of SAS databases for storing
data in the appropriate format. Proper planning of the
SAS database enabled us to run aU aspects of our
analysis with outstanding results.
SAS is a registered trademark of SAS Institute Inc.
in the USA and other countries. ® indicates USA
registration.
Tracy L. Lord
[email protected]
. c/o IBM Microelectronics
Dept. M59/975-1
1000 River Rd.
Essex Junction, Vt. 05452
(802)769-8734
.
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