Better Efficiency and Productivity
OptimalTest’s solutions help you achieve greater utilization, enhanced throughput and better use of your test fleet and global operations while they improve your testing processes and IC products.
Your engineers, test managers and other staff will also realize better efficiency and productivity of up to 20% at wafer sort and final test as a result of our fully automated, intelligent test management solutions including early detection through OT-Detect, which allows organizations to track performance metrics more effectively and helps to ensure maximum values for yield, quality, utilization and throughput are achieved.
For companies with global operations and distributed supply chains, take a look at our dedicated solution that gathers your test data from all of your suppliers in to one central location and enables you to effectively manage and control your supply chain.
For companies that manage their own test floors, we have a variety of dedicated solutions such as our Test Floor Operations solution and OptimalTest Utilization Platform (OT-UP) for OEE.
OptimalTest helps you realize benefits across your operation, from ATE to people, so you can compete better.
Examples
Comparing Test Time Across a Fleet of Testers [Read More]
Finding the Issue
A cross-entity rule created in OT-Rules triggered an alert when significant performance differences were detected between testers.
Performing the Analysis
Cross entity rules compare different entities (such as testers, probe cards and load boards) and highlights equipment with significantly worse performance.
In this case, the rule monitored the average good bin test time and caught slower testers that resulted in low throughput. The specific testers were checked and found to have incorrect settings which were impacting their performance.
Preventing Future Recurrences
A rule was created in OT-Rules to generate an alert when similar conditions surfaced again.
Comparing Test Time Across a Fleet of Testers [
, 241 KB]
Managing Online Retest Settings [Read More]
Finding the Issue
An online retest dashboard in OT-Portal detected that some of the testers are triggering more online retests then others.
Performing the Analysis
Normally, online retest is statistically the same across the test fleet. In this case, based on the data in OT-Portal, the engineers realized that there was a fundamental difference in the way certain testers were performing retest. They checked the online retest policy which was managed by the prober recipe and triggered by certain bin occurrences. They found discrepancies in the settings between different testers. Changing the settings resolved the issue.
Managing Online Retest Settings [, 207 KB]
Maximizing Yield through Probecard Performance Analysis [read more]
Finding the Issue
A high-level product-based report in OT-Portal showed that a product was showing lower than expected yield and higher than expected retest rates.
Performing the Analysis
By drilling down to individual tester performance in OT-Portal, the engineer was able to identify a specific probe card, which was causing the issue within a matter of minutes. The test house was notified and the probe card was removed for inspection.
Preventing Future Recurrences
A rule was created in OT-Rules to automatically catch lots exhibiting high site-to-site yield discrepancies. The next time this problem occurred an email alert was sent immediately to the user so that the problem could be resolved straight away.
Step 1 – Analyzing a probecard showed site 3 with consistently low yield
Step 2 – Creating a rule to catch site-to-site deviations for this product
Step 3 – Receiving an alert when the problem next occurs
Maximizing Yield through Probecard Performance Analysis [, 471 KB]
Preventing Test Time Degradation [Read More]
Finding the Issue
A high-level OT-Portal report showed that a certain product had higher test times than expected. The engineer used detailed touchdown data in OT-Portal to analyze the problem and find the root cause.
Performing the Analysis
OT-Portal was used to view the test time over the duration of the entire lot and the engineer immediately discovered a constant increase in touchdown time.
Preventing Future Recurrences
A rule was created in OT-Rules to generate an alert when similar conditions surfaced again.
Touchdown data chart in OT-Portal showing the lengthening test time
Preventing Test Time Degradation [, 263 KB]
Reducing Wasteful Retests [Read More]
Finding the Issue
OT-Portal showed that while online retest rate was high for a product, the yield reclaimed by retesting the dice was very small. This looked like an opportunity to increase throughput or to pay less retest costs by eliminating wasteful retests (i.e. retest of dice with a low probability of becoming “good”.)
Performing the Analysis
The engineer used the wafer map viewing capabilities of OT-Portal to display a stacked composite wafer map containing the final results after all retests. The small yellow triangles highlight dice which were retested. As is clear from the picture, very few of the dice were reclaimed and 98% of the retest did not result in any bin recovery/bin switching. The retest policy was changed to retest only those bins with significant recovery rates.
Preventing Future Recurrences
Retest recovery rate rules were defined in OT-Rules and are triggered whenever low retest recovery rates are achieved, enabling the user to fix the issue quickly and increase throughput.
Reducing Wasteful Retests [, 341 KB]
Saving Yield by Managing Probe Cards [read more]
The Challenge
A large fabless company tests a number of different products at various test houses around the globe. Each test house maintains its own supply of probe cards for each of the tested products. Using OT-Portal, the fabless company detected that significant yield loss is attributed to poorly maintained probe cards. The company wanted a mechanism to alert when a probe card was nearing the end of its life span or requiring preventative maintenance, before yield loss started to occur.
The Solution
Test data collected in the OptimalTest Database contains an accurate count of touchdowns and cleans performed by each probe card. A “target” was defined in the OptimalTest database for each individual probe card specifying the number of touchdowns at which an alert was to be triggered. When the alert was triggered, a request was sent to the supplier to perform preventative maintenance. When the probe card was re-instated, the target was updated with the new value for the next maintenance cycle.
OT-Portal views were defined to display aging probe cards together with data from probe card related rules showing issues with probe cards. In the first example, the dashboard shows rule alerts by probe card with a summary of the top “offender” probe cards that triggered the most alarms for yield/quality issues. The second example contains information about each probe card with its performance and health status including total touchdown count, average lot yield by site, bin summary by site etc… It also shows the target spec for each probe card and its status in relation to that spec (the colored column.
Saving Yield by Managing Probe Cards [, 422 KB]
Tracking Suppliers with Operational Dashboards [Read More]
The Challenge
A large fabless company uses a number of suppliers to perform its testing operations. The fabless company’s management team needed timely data and the ability to view and compare numerous metrics (such as yield, first-pass yield, throughput, retest rates, etc.) across all suppliers at a glance.
The Solution
OT-Portal widgets were used to create several product- and supplier-related dashboard views with charts for each of the key metrics, showing current status and recent trends. The dashboards were saved as templates and shared with other teams. Now, users can view the dashboard at any time and get a clear picture of the current status based on the most recent data.
Tracking Suppliers with Operational Dashboards [, 449 KB]
Using Rules to Catch ATE Issues [Read more]
Finding the Issue
A by-8 probecard was failing on all sites in some touchdowns due to a tester issue. This problem was detected automatically by an offline touchdown-monitoring rule defined in OT-Rules. Standard yield monitoring mechanisms failed to discover the issue because wafer yield was still above the acceptable threshold.
Performing the Analysis
The user received an alert via email minutes after wafer probing was completed. The user viewed the OT-Portal wafermap tool and informed the test house. The wafer was re-probed, significant yield was reclaimed and the user was able to view the results of the retest in OT-Portal. The tester was investigated to find the root-cause of the problem.
Using Rules to Catch ATE Issues [, 595 KB]
Using Targets for Planning and Capacity Tracking [read more]
The Challenge
To accurately forecast the number of testers required over the next few months, planners needed a mechanism to calculate the throughput of testers on their products (measured in Units per Hour – UPH). Planners wanted to make sure that the real throughput of the testers matched the engineers’ expectations.
The Solution
A table of monthly targets was defined for the UPH measure in OT-Portal. The target UPH was specified for each of the products being tested. An OT-Portal report was created to display the actual UPH (based on real test data) and compare it to the pre-specified target. The OT-Portal view immediately highlighted testers that were under performing. These testers were investigated and the plans were adjusted to allow for the shortfall.
Using Targets for Planning and Capacity Tracking [, 202 KB]
Using Targets to Manage Operations [read more]
The Challenge
Different products, suppliers and processes are measured in different ways depending on the maturity of the process or product and many other factors. A large fabless company was looking for a way to determine exactly how well they and their suppliers were performing over a range of well-defined metrics.
The Solution
Using the OptimalTest Targets system, various families of key performance indicators were defined and the data was made available in OT-Portal. Managers now see the data with clear highlighting of issues for each type of display.
In the first example below, the throughput per product is displayed relative to the target throughput for that product.
In the second example, the throughput is measured per supplier and the system manages the expected throughput for each supplier separately. This example shows a serious shortfall in Facility_A during the third week of the year.
In the third example, specific targets have been set per bin for a product where speed-binning is implemented. The system clearly shows if the expected yields for each bin are being met.
Using Targets to Manage Operations [, 407 KB]
end faq
