PCBA AOI Defect Log and DPMO Worksheet
Raw defect counts are hard to compare across assemblies that have 300 or 3,000 solder joints. DPMO normalizes the count to a million opportunities so you can compare lots, shifts and products, and show a trend. This worksheet keeps the log and calculates it for you.
| Lot / shift | Date | Boards inspected | Opportunities per board | Defects found | Top defect | DPMO | Defects per board | Short-term sigma level | |
|---|---|---|---|---|---|---|---|---|---|
107 | 0.152 | 5.2018 | |||||||
57 | 0.0367 | 5.3574 | |||||||
125 | 0.08 | 5.1623 | |||||||
220 | 0.5083 | 5.0148 | |||||||
35 | 0.0225 | 5.4752 |
DPMO is defects divided by total opportunities, times one million. Agree on one counting rule for opportunities (for example IPC-9261 style: each component termination, each placement and each solder joint) and keep it fixed, otherwise comparisons between lines or customers are meaningless. The sigma column uses the common 1.5 sigma shift convention.
Trend defects across lines and ERP lots
Optional: discuss joining inspection data with ERP lots and routings.
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How to use the log
Enter one row per lot or shift. Add the number of boards inspected, the opportunities per board according to your counting rule, and the defects found at AOI or final inspection. Choose the dominant defect type so a Pareto of causes is easy to build later in Excel. The sheet returns DPMO, defects per board and a short-term sigma level for each row, plus totals for the log.
- Use the same inspection point for all rows (post-reflow AOI, for example).
- Count each defect once, not once per inspector.
- Do not drop lots with zero defects; they pull the average to its true value.
- Record false calls separately from true defects.
The formula and standard
DPMO equals defects divided by boards times opportunities per board, times 1,000,000. IPC-7912 describes DPMO and related metrics for printed board assemblies, and IPC-9261 covers in-process DPMO and estimated yield. The sigma level here is the normal inverse of one minus the defect fraction plus the usual 1.5 sigma shift. It is a convenience figure for dashboards, not a capability study.
Reading the example
WO-118201 shows 38 defects over 250 boards at 1,420 opportunities per board, about 107 DPMO. WO-118203 has fewer boards, but with 61 defects over 2,310 opportunities it lands near 220 DPMO and is the worst lot, with misalignment as the main cause. Shift 2 on WO-118202 is more than twice as bad as shift 1 for the same assembly, which points to a shift-dependent cause such as setup, material handling or paste age.
Common mistakes
The biggest mistake is inconsistent opportunity counting, which makes DPMO look better or worse for no real reason. Another is mixing AOI false calls into defect counts, which inflates DPMO and teaches operators to ignore the data. Last, do not set a target without a baseline; collect several weeks of data first, then set DPMO goals by assembly family.
Frequently Asked Questions
What is a good DPMO for SMT assembly?
It depends on the opportunity counting rule, the product complexity and the customer. Many high volume lines target well under 100 DPMO at final inspection, while complex boards with fine pitch and BGA may run higher. Trend and compare within your own rules.
How do I count opportunities?
A common rule is one opportunity per component placement plus one per solder termination, and sometimes per through-hole joint. Document the rule, apply it to every assembly and keep it stable. IPC-9261 gives a framework many EMS companies follow.
Why is sigma level shown with a 1.5 shift?
Six Sigma convention assumes the process mean drifts by 1.5 sigma over the long term, so a short-term sigma figure is reported with that adjustment. It is useful for quick comparison but should not replace a proper process capability study.
Should DPMO include rework?
Count defects found before rework, since that reflects the process. Final escapes after test belong in a separate metric. Mixing the two hides where defects actually arise.
If your inspection results sit in AOI, MES or ERP systems, we can consolidate them into one on-prem view with trend and Pareto analysis.
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