Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Entity–attribute–value model wikipedia , lookup
Extensible Storage Engine wikipedia , lookup
Microsoft Jet Database Engine wikipedia , lookup
Concurrency control wikipedia , lookup
Relational model wikipedia , lookup
Functional Database Model wikipedia , lookup
ContactPoint wikipedia , lookup
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. 607 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. 608 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 . 609