Design for testability (DFT) in PCB Design - Real Business Scenario

Scenario Mode

👤 Your Role: You are a PCB design engineer working with the quality assurance team.

📋 Request: Your manager wants a report showing how well the current PCB designs follow Design for Testability (DFT) principles to reduce manufacturing defects.

📊 Data: You have data on multiple PCB designs including test points count, boundary scan usage, test coverage percentage, and defect rates.

🎯 Deliverable: Create a dashboard that visualizes DFT metrics per design and highlights designs with low test coverage and high defect rates.

Sample Data

Design_ID	Test_Points	Boundary_Scan	Test_Coverage_%	Defect_Rate_%
PCB001	15	Yes	85	2.5
PCB002	8	No	60	7.0
PCB003	20	Yes	90	1.0
PCB004	5	No	50	8.5
PCB005	12	Yes	80	3.0
PCB006	7	No	55	6.5
PCB007	18	Yes	88	2.0
PCB008	6	No	58	7.5

1

Step 1: Load the PCB design data into your BI tool.

Import the table with columns: Design_ID, Test_Points, Boundary_Scan, Test_Coverage_%, Defect_Rate_%

Expected Result

Data table with 8 PCB designs loaded successfully.

2

Step 2: Create a calculated measure 'Low_Test_Coverage' to flag designs with test coverage below 70%.

Low_Test_Coverage = IF([Test_Coverage_%] < 70, "Yes", "No")

Expected Result

Designs PCB002, PCB004, PCB006, and PCB008 flagged as 'Yes' for low test coverage.

3

Step 3: Create a calculated measure 'High_Defect_Rate' to flag designs with defect rate above 5%.

High_Defect_Rate = IF([Defect_Rate_%] > 5, "Yes", "No")

Expected Result

Designs PCB002, PCB004, PCB006, and PCB008 flagged as 'Yes' for high defect rate.

4

Step 4: Build a table visualization showing Design_ID, Test_Points, Boundary_Scan, Test_Coverage_%, Defect_Rate_%, Low_Test_Coverage, High_Defect_Rate.

Configure table with mentioned columns and calculated measures.

Expected Result

Table clearly shows which designs have low test coverage and high defect rates.

5

Step 5: Create a bar chart showing Test_Coverage_% per Design_ID with a color code: red for coverage below 70%, green otherwise.

Set conditional formatting on bars based on Test_Coverage_% threshold 70%.

Expected Result

Bar chart visually highlights designs with insufficient test coverage.

6

Step 6: Create a scatter plot with Test_Coverage_% on X-axis and Defect_Rate_% on Y-axis, each point labeled by Design_ID.

Plot Test_Coverage_% vs Defect_Rate_% with labels.

Expected Result

Scatter plot shows inverse relationship between test coverage and defect rate.

7

Step 7: Add filters to the dashboard to select designs by Boundary_Scan usage and Low_Test_Coverage flag.

Add slicers for Boundary_Scan (Yes/No) and Low_Test_Coverage (Yes/No).

Expected Result

User can filter dashboard to focus on designs missing boundary scan or with low coverage.

8

Step 8: Review dashboard and highlight designs needing improvement in DFT to reduce defects.

Use visual cues and table flags to identify PCB002, PCB004, PCB006, PCB008 as priority.

Expected Result

Dashboard clearly identifies designs with poor DFT metrics and high defects.

Final Result

Bar chart: Test Coverage % per Design_ID with red bars for <70%

↓

Scatter plot: Test Coverage % vs Defect Rate % showing inverse trend

✓Designs without boundary scan and with low test coverage have higher defect rates.

✓PCB002, PCB004, PCB006, and PCB008 need improved DFT to reduce defects.

✓Higher test coverage correlates with lower defect rates in PCB designs.

Bonus Challenge

Add a calculated measure to estimate potential defect reduction if low coverage designs improve test coverage to 80%.

Show Hint

Create a measure that calculates the difference in defect rate assuming test coverage increases to 80%, then sum potential defect reduction.