Why power integrity ensures reliable operation in PCB Design - Business Case Study

Scenario Mode

👤 Your Role: You are a PCB design engineer analyzing power delivery

📋 Request: Your manager wants a report showing how power integrity affects the reliability of PCB operation

📊 Data: You have measurements of voltage levels, noise, and failure rates from several PCB prototypes under different power integrity conditions

🎯 Deliverable: Create a dashboard that visualizes the relationship between power integrity metrics and PCB reliability

Sample Data

PCB_ID	Voltage_Stability_V	Noise_Level_mV	Failure_Rate_%	Power_Integrity_Status
PCB_01	3.3	5	1	Good
PCB_02	3.1	15	5	Moderate
PCB_03	3.0	20	8	Poor
PCB_04	3.3	4	0.5	Good
PCB_05	3.2	10	3	Moderate
PCB_06	2.9	25	10	Poor
PCB_07	3.3	3	0.2	Good
PCB_08	3.1	12	4	Moderate

1

Step 1: Create a table visual showing all PCB prototypes with columns: PCB_ID, Voltage_Stability_V, Noise_Level_mV, Failure_Rate_%, Power_Integrity_Status

Use the sample data as is in a table visual

Expected Result

Table displays 8 rows with all columns correctly shown

2

Step 2: Create a bar chart showing average Failure_Rate_% grouped by Power_Integrity_Status

Aggregate Failure_Rate_% by Power_Integrity_Status using average

Expected Result

Bar chart shows average failure rates: Good ~0.57%, Moderate ~4%, Poor ~9.0%

3

Step 3: Create a scatter plot with Voltage_Stability_V on X-axis, Noise_Level_mV on Y-axis, and bubble size representing Failure_Rate_%

Plot each PCB_ID as a point with size proportional to Failure_Rate_%

Expected Result

Scatter plot shows that lower voltage stability and higher noise correspond to larger failure rates

4

Step 4: Add a slicer filter for Power_Integrity_Status to allow filtering the visuals by Good, Moderate, Poor

Add slicer on Power_Integrity_Status column

Expected Result

User can filter visuals to see data for each power integrity status

5

Step 5: Create a calculated measure 'Average Failure Rate' with formula: AVERAGE('Table'[Failure_Rate_%])

Average Failure Rate = AVERAGE('Table'[Failure_Rate_%])

Expected Result

Measure returns overall average failure rate of 3.96%

6

Step 6: Create a card visual to display the 'Average Failure Rate' measure

Use the measure created in step 5

Expected Result

Card shows 3.96% average failure rate

7

Step 7: Interpret the dashboard: Explain that better power integrity (Good status) leads to lower failure rates and more stable voltage with less noise

Use insights from visuals

Expected Result

Clear understanding that power integrity ensures reliable PCB operation by reducing failures

Final Result

Power Integrity Dashboard

+----------------------------+
| PCB Data Table             |
| [PCB_ID, Voltage, Noise,   |
|  Failure Rate, Status]     |
+----------------------------+

+----------------------------+
| Avg Failure Rate by Status |
| Good: 0.57%                |
| Moderate: 4%               |
| Poor: 9%                   |
+----------------------------+

+----------------------------+
| Voltage vs Noise Scatter   |
| Bubble size = Failure Rate |
+----------------------------+

+----------------------------+
| Average Failure Rate Card  |
| 3.96%                      |
+----------------------------+

+----------------------------+
| Power Integrity Slicer     |
| [Good] [Moderate] [Poor]  |
+----------------------------+

✓PCBs with Good power integrity have the lowest failure rates and stable voltage with minimal noise.

✓Moderate power integrity shows increased noise and failure rates.

✓Poor power integrity results in high noise, unstable voltage, and the highest failure rates.

✓Maintaining power integrity is critical to ensure reliable PCB operation and reduce failures.

Bonus Challenge

Add a time series line chart showing how failure rates change over multiple test cycles for each power integrity status.

Show Hint

You will need to add a 'Test_Cycle' column with dates or cycle numbers and plot Failure_Rate_% over time grouped by Power_Integrity_Status.