Test Acceleration for Hardware Teams

Test Acceleration for Hardware Teams with TofuPilot

Hardware teams don't need to test more. They need to test faster. The bottleneck isn't usually the test itself. It's the time spent searching for data, debugging failures manually, and waiting for reports. TofuPilot eliminates these delays.

Where Hardware Teams Lose Time

The test run itself might take 60 seconds. Everything around it takes hours or days. That's where acceleration happens.

Accelerating Failure Debug

Before TofuPilot

1. Unit fails on the test station
2. Operator calls the test engineer
3. Test engineer walks to the station, looks at the screen
4. Test engineer manually records the failing measurements
5. Test engineer goes back to their desk, opens old data files to compare
6. Test engineer emails the design team with findings
7. Back and forth continues

With TofuPilot

1. Unit fails on the test station
2. Test engineer opens TofuPilot, filters to the failed run
3. Compares measurements against recent passing runs in one click
4. Identifies the anomalous measurement
5. Checks the measurement trend to see when it started
6. Shares the dashboard link with the design team

Step 2-6 takes 5 minutes. The old way takes half a day.

Accelerating Test Development

Data-Driven Limit Setting

Instead of guessing limits from datasheets, run 50 units and use the actual distribution to set limits.

import numpy as np
from tofupilot import TofuPilotClient

client = TofuPilotClient()

# Get pilot production data
runs = client.get_runs(
    procedure_id="PILOT-FUNCTIONAL",
    limit=50,
)

# Extract values for each measurement
measurements = {}
for run in runs:
    for step in run.get("steps", []):
        for m in step.get("measurements", []):
            if m["name"] not in measurements:
                measurements[m["name"]] = []
            measurements[m["name"]].append(m["value"])

# Calculate recommended limits (mean +/- 4 sigma)
for name, values in measurements.items():
    arr = np.array(values)
    mean = np.mean(arr)
    std = np.std(arr, ddof=1)
    print(f"{name}: mean={mean:.4f}, std={std:.4f}")
    print(f"  Recommended limits: [{mean - 4*std:.4f}, {mean + 4*std:.4f}]")

This replaces weeks of limit tuning with 30 minutes of data analysis.

Identifying Redundant Tests

Not every measurement adds value. Some measurements never fail. Some always correlate perfectly with another measurement (testing the same thing twice).

Pull your measurement data from TofuPilot and check:

Cutting redundant measurements directly reduces cycle time and increases throughput.

Accelerating Yield Improvement

The Improvement Loop

TofuPilot accelerates every step:

1. Measure: Automatic data collection from every station
2. Analyze: Dashboards show yield trends, failure paretos, and measurement distributions
3. Improve: Data points to the root cause, so fixes are targeted
4. Verify: Before/after comparison confirms the fix worked

Without centralized data, steps 1 and 2 take days. With TofuPilot, they're instant.

Prioritizing Improvements

The failure pareto in TofuPilot shows you where to focus. Fix the #1 failure mode first. It has the biggest yield impact.

Don't try to fix everything at once. Fix one thing, verify the improvement in TofuPilot, then move to the next.

Accelerating NPI (New Product Introduction)

During NPI, test development and product development happen in parallel. Every design revision needs updated tests. Every test result informs the next design revision.

TofuPilot accelerates this loop by:

• Storing results from every prototype build
• Comparing measurements across design revisions
• Showing which design changes improved (or regressed) specific measurements
• Providing immediate visibility into whether a new build meets specs

Comparing Design Revisions

from tofupilot import TofuPilotClient

client = TofuPilotClient()

# Get runs from two design revisions
rev_a_runs = client.get_runs(procedure_id="PROTO-FUNCTIONAL", limit=20)
rev_b_runs = client.get_runs(procedure_id="PROTO-FUNCTIONAL", limit=20)

# Compare measurement means
# Filter by part_number or date range to separate revisions

If Rev B has 15% lower idle current and 5% tighter voltage regulation, the design change worked. If thermal measurements regressed, the new layout needs attention. TofuPilot shows this in minutes, not days.

The Compound Effect

Each individual acceleration seems small: 5 minutes saved here, 30 minutes there. But across a team of 5 test engineers running 10 debug sessions per week, the compound effect is significant.

That's 400 engineering hours per month redirected from data gathering to actual engineering work: improving designs, optimizing processes, and shipping better products.