The Industry Leader in Real-Time, Adaptive Test
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Pintail Technologies is a company founded in 2000 with the mission of attacking the increasing cost of semiconductor test by means of unique, real-time, statistically-based, software solutions. Pintail Technologies is recognized throughout the industry as a pioneer and leader in real-time, adaptive test technology for semiconductor manufacturing. These solutions have proven effective at reducing the cost of test, improving quality, and improving the efficiency of test operations.
A groundbreaking approach
Traditionally, the role of test has been supported by tools built on an antiquated database scheme that supports file-based, post-processing solutions. In contrast, Pintail developed a core technology based upon a revolutionary way of accessing and processing data in real-time.
In order to accomplish this, several obstacles had to be overcome:
- How could a third-party vendor add software to modern automatic test equipment and not slow the testers down by using precious compute resources?
- How could necessary decision-making be accomplished within the indexing time in hi-volume production lines?
- How could all of this be made to work within the complex networking and IT infrastructures of modern semiconductor manufacturers without jeopardizing the efficiency and reliability of the lines?
- With the continued movement to outsourcing test services, would the industry support such technology across the supply chain?
Addressing these obstacles and more, Pintail developed a powerful, tightly integrated suite of applications that can improve yield learning, cost of test, reliability, and production efficiencies.
Proven in production
Today, Pintail leads the industry with more production experience than any other company in the test optimization field. In fact, dozens of Pintail solutions are operating successfully around the world, serving IDMs as well as fabless, test and assembly, and ATE manufacturing companies. Pintail solutions have been repeatedly proven to reduce costs, lower defective parts per million (DPPM), improve yield learning, optimize efficiency and provide better visibility across the supply chain.
