A Brief Overview and Importance of Outlier Detection Method in Semiconductor Manufacturing

1. A Brief Overview and Importance of Outlier Detection Method in Semiconductor Manufacturing Outlier detection is a technique used to identify unusual observations, data points that don't fall within the distribution. It's often used in SPAT semiconductor testing, where it can help identify manufacturing defects or variations and classify them as rejects although passing a set of specified criteria. It identifies test results that are outside of the normal range. Outlier detection helps semiconductor manufacturers to only ship high-quality semiconductor devices. Outliers can indicate a problem on a particular device under test. For example, if most of the devices under test are behaving within normal parameters except for some that have significantly higher or lower results, there is a high probability that these devices are latent defects. Outlier detection test can help identify these potential problems so that they can be screened out and not used in the intended application. Importance of Outlier Detection Method in Semiconductor Industry Semiconductor ICs or microchips are used in almost every major industry like automotive, consumer electronics, smartphones, gaming consoles, high-tech military equipment and more. This is the reason that ensuring the quality of each and every chip is crucially important for semiconductor data manufacturers. These chips perform some key functions in important fields. For example, various sensors that work with the help of microchips initiate the air bag protection function in cars during a car crash. If the airbag doesn’t open on time because of a faulty sensor, lives can be lost. Let’s take another example of smartphones. Smartphone manufacturers have to face a lot of competition from other manufacturers that manufacturing yield. They cannot compromise on quality. Suppose they launch a high-end mobile phone in the market but some of the phones are shipped with faulty processors. This will cause consumer returns, negative reputation and market share loss. No one wants a faulty chip because of the risks associated with it.

2. This is why outlier detection method should be used in semiconductor testing. It removes the dice that pass standard tests but deviate grossly from the normal distribution. Various statistical algorithms are applied to find these outliers so they are never shipped to the customer.