Do You Know Your True First Pass Yield?
From Output Yield to First Pass Yield Explained.

True First Pass Yield

First Pass Yield (FPY) is one of the most important metrics in manufacturing, but the definitions vary significantly from company to company, and that variation has consequences.

A standard definition that is gaining traction is what some refer to as True First Pass Yield. In this article we look closely at what that means, why it matters, and what it takes to use FPY as something more than a reporting number.

In general, True First Pass Yield is based on all test results and includes even trivial test failures. It is unique to each test operation your product goes through. And it is not something you can manage through Microsoft Excel.

We'll discuss the benefits for companies to consistently track and optimize True FPY, and look at the premises needed to ensure it functions as a metric that guides informed continuous improvement rather than a selective vanity number.

What Are the Different Types of Yield?

Broadly speaking, there are four types of yield used in manufacturing. The difference between these lies in when a failure is first recognized.

The four types of yield:

• Output Yield
• Rework Yield
• First Pass Yield
• Batch Yield

Not all yield metrics are the same, and understanding where each one sits in relation to your test data is the starting point for using any of them meaningfully.

Output Yield

With Output Yield you only consider the failures that cause you to scrap a unit. This type of yield can easily run close to 100%.

Rework Yield

The Rework Yield metric is traditionally more common. Here you recognize a failure when the unit goes to rework or repair. Part of the reason why this is a common approach is that most EMS companies adopted MES software technology early on, and repair documentation is a core feature of these systems. With repair information and production quantities already in the MES system, this metric becomes very simple to calculate. Rework Yield can also easily trend high, often above 90%.

Rework Yield and Output Yield

Both Rework Yield and Output Yield are often referred to as First Pass Yield. That is incorrect. True First Pass Yield in the context of manufacturing is a test-centric metric.

Yield recognition should come from the results of the test stations and include any and all scenarios where the test sequencer flags the test as failing.

Without this view, your results when compared to a company actively managing True First Pass Yield will fall short when it comes to:

• Transparency
• Accountability for different stakeholders that contribute to building products
• Productivity
• Product quality
• Profitability

In any and all scenarios we are also including trivial test failures, such as the operator forgetting to power on an instrument. If these things happen often enough to have statistical significance they should definitely be on your radar.

Batch Yield

Batch Yield is a variant of First Pass Yield based on batch ID rather than serial numbers. This is most common very early in the build process when a unit has still not been assigned a unique serial number.

Because the serial number needed for a unit-centric yield calculation is either static (NOVALUE) or random, a random number for each test run including retests, your ability to extract actionable information from the First Pass Yield metric is significantly limited.

Either all failures except one will belong to test runs later than the first attempt, or all will be counted as first-attempt failures.

For a detailed breakdown of how yield is calculated at the unit level, including the distinction between test report yield and unit level yield and why the way a time filter is applied can significantly affect the accuracy of your numbers, read our article Unit Yield vs Test Report Yield.

How True First Pass Yield Surfaces Areas for Improvement

Your company might follow methodologies such as Lean Six Sigma or Total Quality Management, or even no formalized framework at all. Either way, there are almost certainly wasteful activities in your company, or its supply chain, that are causing damage to your productivity and profitability.

These wasteful activities vary a lot between industries, but the most common factor is human error. Manufacturing very often uses product-centric KPIs to monitor performance. But these metrics tend to occur late in the process flow, and although they touch on the most important performance metrics of your product, they are not the only determinants of whether a product will fail in the field.

To capture the full value, you need a scope wide enough to identify all inefficiencies and trivial failures at the point where they originate and proactively managing True First Pass Yield does that.

Metrics and visibility that are efficiency and effectiveness focused give you much better opportunities to intervene early and provide proper guidance to your factory and engineering teams.

A design-related decision might cause the unit under test to be difficult to connect properly to the test system, producing frequent failures as a result. It doesn't go to rework. It passes the test eventually. It's an efficiency issue, not a product issue. Regardless of the type, it is an issue and it needs attention.

True First Pass Yield Reveals the Hidden Cost of Retesting

A very common problem in electronics manufacturing is excessive retesting. A product fails a test and is tested again. Once or twice is unremarkable, but when a test is redone ten times it is clearly a problem, and without the right data it's a problem nobody can see.

The retest doesn't have to pose a quality concern. Often the product is completely fine and the weakness is in the test system. Perhaps the test interface board has an intermittent connection that the operator has learned to solve by wiggling the cables. The product passes on the eleventh attempt, the unit moves on, and the behavior is allowed to persist and establish itself as normal.

But imagine that the next time you have an issue with the product itself. Poor connectivity between a PCB and an antenna input. The operator reruns the test over and over. Perhaps he applies some pressure on the connector on the sixth attempt. That again unintentionally gives better connectivity between the two components. Test passed, unit shipped.

With full visibility into the underlying failures, their measurements, associated repair operations and warranty cases, this behavior doesn't get the chance to take hold.

Why First Pass Yield Gets Less Attention Than It Should

One of the more revealing things about how manufacturers approach yield is how rarely anyone fully owns First Pass Yield as a business metric.

Test engineers are focused on their station. Their job is to make sure the test sequence is working, the limits are correctly set, and the product gets through. If a unit fails and needs to be retested, that's part of the process. Retesting isn't their problem specifically, and FPY isn't the metric they're judged on. They want detailed information about their test machines, visibility into specific test steps, and the ability to identify root causes at the station level.

Quality managers, meanwhile, are often watching Last Pass Yield, a measure of whether units eventually passed rather than whether they passed first time. If LPY is close to 100%, the product is getting through and the customer is receiving what they expect. In that context it's easy to ask why FPY deserves much attention at all. Customers expect close to 100% and when it's anything other than that, there's concern. But LPY close to 100% is not the same as a healthy operation.

The business is absorbing the cost of every retest, every extra minute of operator time, every unnecessary pass through a station. That cost doesn't show up in LPY. It only surfaces when you track each unit by serial number through every run it took. And when manufacturers see that data properly calculated for the first time, the numbers are frequently not what they expected.

That gap is usually where the most useful improvements are found.

The Relationship Between First Pass Yield and Test Coverage

Your First Pass Yield metric is an indicator, not an absolute measure, and it has an inverse relationship with your test coverage. When test coverage goes down, fewer failures are detected and the yield metric improves. That improvement is not a signal of progress.

Test coverage needs to be actively managed alongside FPY rather than treated as a fixed backdrop. The best time to do this is during the New Product Introduction phase, before high volumes begin and before attention shifts to the next product release. But it is never too late.

There is a practical dimension to this that many manufacturers encounter when limits set during development reach the production floor. Test limits from R&D are typically derived from a small number of units tested under controlled conditions. When those limits reach production they encounter a different reality: different test machines, different cables, different operators, different timing, and the accumulated variance that live manufacturing introduces.

Limits that worked in development frequently produce poor yield in early production, and the instinct is often to adjust the way the test is run rather than to revisit the limits themselves. Production limits need to be grounded in production data, not inherited unchanged from development.

Process Capability Analysis is a good tool for this exercise. When combined with measurement trend visualization it is easy to prioritize and differentiate between the tests that have the potential to be problematic and those that in reality allow faulty products to leave the factory.

At one time, a Quality Assurance Manager in a factory turned off the monitors displaying their real-time yield metrics because they were consistently below target. This mindset is a direct threat to data-driven continuous improvement, where the focus shifts to managing personal metrics rather than what is good for the company.

First Pass Yield Is Not About Improving Quality

If you take all factors into account, yield improvements and improved test coverage are generally not pursued in order to improve the quality of your products. They are pursued to make sure you have the right quality.

To give an example: imagine you had infinite resources. You could deploy QA initiatives rigorous enough to ensure no flawed product ever left the factory and hire the best data engineers available to handle analytics. But the process of getting there would obviously be very expensive, more so than the money saved from reducing warranty claims.

In general, if you go from 90% First Pass Yield to 100%, at some point the marginal profit will go negative. Your added quality improvements become too expensive. At this point you should move from improving yield and coverage to maintaining it.

So rather than being about improving quality, it is about optimizing quality. Finding the test coverage and yield target that is right for your company, your industry, and the quality expected by your customers.

Rolled Throughput Yield vs First Pass Yield

Most likely your product goes through several processes, with different tests for each. This can be tests for subcomponents, PCB and PCBA tests, in-circuit testing, burn-in testing, final function test, or any other relevant test. Each of these has its own First Pass Yield, as this is a metric that relates to the individual test processes.

Rolled Throughput Yield tells you what percentage of your products goes through all of their processes without failing. And here lies a problem that affects almost all companies with outsourced manufacturing.

Even if you have standardized on True First Pass Yield as your primary KPI, what if your PCB suppliers use Last Pass Yield? Perhaps they operate less efficiently than you, but how can you know that the same quality issues from poor test coverage and retesting are not being transferred to you? The answer is that you can't. You need to centralize and standardize the data, with one portal and one philosophy governing it all.

Why Transparency in First Pass Yield Data Drives Accountability

Since the yield is only indicative, you need transparency into the underlying data. You need to be able to slice and dice it as you wish and understand the contextual relationships at play.

Which products have the most failures? Which revisions? How does test station A compare to B, or how do factories compare? What were the common trends during manufacturing for a batch of units returned under warranty?

This transparency and visibility help to foster accountability in your organization and across your supply chain.

A yield percentage in a spreadsheet can't answer these questions. A spreadsheet can produce a number and track it over time, but it can't show you the contextual relationships that make the number meaningful. It can't tell you whether a drop in FPY is concentrated at one station or spread across the line. It can't correlate test failures with repair outcomes or flag patterns that only become visible when you look across units and products rather than individual reports.

Visibility into the underlying data is what makes accountability possible, across teams, across factories, and across your whole supply chain.