Solder mask expansion in PCB Design - Real Business Scenario

Scenario Mode

👤 Your Role: You are a PCB manufacturing analyst at an electronics company.

📋 Request: Your manager wants you to analyze solder mask expansion data to ensure quality control and optimize the manufacturing process.

📊 Data: You have data on solder mask expansion measurements for different PCB batches, including batch ID, measured expansion in micrometers, target expansion, and pass/fail status.

🎯 Deliverable: Create a report showing average solder mask expansion per batch, highlight batches that failed quality standards, and visualize trends to help the manufacturing team improve processes.

Sample Data

Batch ID	Measured Expansion (µm)	Target Expansion (µm)	Status
B001	120	100	Fail
B002	95	100	Pass
B003	105	100	Pass
B004	130	100	Fail
B005	98	100	Pass
B006	110	100	Fail
B007	102	100	Pass
B008	99	100	Pass

1

Step 1: Import the solder mask expansion data into your BI tool.

Load the table with columns: Batch ID, Measured Expansion (µm), Target Expansion (µm), Status.

Expected Result

Data table with 8 rows and 4 columns loaded successfully.

2

Step 2: Create a calculated measure to find the average measured expansion across all batches.

Average Expansion = AVERAGE('Data'[Measured Expansion (µm)])

Expected Result

Average Expansion value calculated as 107.375 µm.

3

Step 3: Create a filter or slicer to show only batches that failed the quality check.

Filter where Status = 'Fail'

Expected Result

Filtered view showing batches B001, B004, and B006.

4

Step 4: Build a bar chart with Batch ID on the X-axis and Measured Expansion on the Y-axis, color-coded by Pass/Fail status.

X-axis: Batch ID; Y-axis: Measured Expansion (µm); Legend: Status

Expected Result

Bar chart showing each batch's expansion with red bars for Fail and green bars for Pass.

5

Step 5: Add a reference line on the bar chart at the Target Expansion value (100 µm) to compare measured values.

Add constant line at 100 µm

Expected Result

Reference line visible at 100 µm on the chart.

6

Step 6: Create a summary table showing Batch ID, Measured Expansion, Target Expansion, Status, and a new column 'Difference' calculated as Measured Expansion minus Target Expansion.

Difference = 'Data'[Measured Expansion (µm)] - 'Data'[Target Expansion (µm)]

Expected Result

Table with difference values: B001=20, B002=-5, B003=5, B004=30, B005=-2, B006=10, B007=2, B008=-1.

7

Step 7: Interpret the results to identify which batches need process review due to high solder mask expansion.

Review batches where Difference > 5 µm and Status = 'Fail'

Expected Result

Batches B001, B004, and B006 identified for process review.

Final Result

Solder Mask Expansion Report

Batch ID | Measured Expansion (µm) | Status | Difference (µm)
-----------------------------------------------------------
B001     | 120                      | Fail   | +20
B002     | 95                       | Pass   | -5
B003     | 105                      | Pass   | +5
B004     | 130                      | Fail   | +30
B005     | 98                       | Pass   | -2
B006     | 110                      | Fail   | +10
B007     | 102                      | Pass   | +2
B008     | 99                       | Pass   | -1

Bar Chart: Measured Expansion by Batch (Red=Fail, Green=Pass)
Reference Line at 100 µm

Average Expansion: 107.375 µm

Batches B001, B004, B006 require process review.

✓Average solder mask expansion is higher than the target of 100 µm.

✓Three batches (B001, B004, B006) failed quality due to excessive expansion.

✓Batches with expansion difference greater than 5 µm mostly failed.

✓Visualizing data helps quickly identify problem batches for manufacturing review.

Bonus Challenge

Create a trend line chart showing solder mask expansion over time if batch dates are available, to detect any improvement or degradation in the process.

Show Hint

Add a 'Batch Date' column to your data and plot Measured Expansion against Batch Date with a line chart.