Hardware Test Observability with TofuPilot

Hardware Test Observability with TofuPilot

Software teams have Datadog and Grafana. Hardware test teams have... shared drives full of CSV files. Test observability means having the same real-time visibility into your hardware test operations that software teams take for granted.

What Test Observability Means for Hardware

Observability in software is about understanding system behavior from its outputs (logs, metrics, traces). Hardware test observability applies the same principle: understand your production quality from test outputs (measurements, pass/fail rates, cycle times).

The Three Pillars of Hardware Test Observability

1. Metrics

Quantitative data about your test operations.

• First-pass yield (FPY) per procedure, station, and time period
• Measurement distributions for every parameter you test
• Cpk/Ppk process capability indices
• Test cycle time and throughput
• Failure pareto showing top failure modes

TofuPilot computes these automatically from your test data. No spreadsheet formulas, no manual aggregation.

2. Logs

Every test run is a log entry. TofuPilot stores:

• Full measurement data with limits and units
• Pass/fail status per step and per run
• Timestamps, station identifiers, operator info
• Attachments (waveforms, images, log files)
• Unit metadata (serial number, revision, batch)

Unlike CSV files on a shared drive, these logs are indexed, searchable, and queryable through the dashboard and API.

3. Traces

A unit's journey through your test process is a trace. TofuPilot links all test runs for a given serial number, showing:

• Which tests the unit has passed
• Which station ran each test
• When each test was executed
• The complete measurement history

This is critical for units that go through multiple test stages (ICT, functional, burn-in, final test). The full trace tells you everything that happened to a unit from first test to ship.

Building Observability with TofuPilot

Step 1: Centralize All Test Data

Every test station should push results to TofuPilot. This eliminates data silos.

from tofupilot import TofuPilotClient

# Same client works for every station
client = TofuPilotClient()

# Every run is automatically indexed by procedure, station, and unit
client.create_run(
    procedure_id="FINAL-TEST-V2",
    unit_under_test={"serial_number": "UNIT-8847"},
    run_passed=True,
    steps=[...],
)

Step 2: Use Dashboards for Real-Time Visibility

TofuPilot's procedure dashboard shows live metrics:

• Current FPY with trend line
• Measurement distributions with limit overlays
• Recent run history with pass/fail status
• Failure mode breakdown

Pin the dashboards on a monitor near the production line. When yield drops, everyone sees it immediately.

Step 3: Set Up Yield Monitoring

Track FPY over time to catch regressions early. A yield drop from 98% to 94% over a week is easy to miss in daily noise but obvious in a trend chart.

Step 4: Enable Unit Traceability

When a field return comes in, search by serial number in TofuPilot. Every test the unit ever ran is there: measurements, pass/fail status, timestamps, and which station tested it. No digging through old files.

Observability vs. Reporting

Reporting tells you what happened last month. Observability tells you what's happening right now.

Common Observability Wins

Catching a yield drop in hours, not weeks. Without observability, yield problems surface in weekly quality reviews. With TofuPilot's live dashboards, engineers see the drop the same day it starts.

Finding station-specific issues. One station has 91% FPY while the others run at 98%. Without centralized data, this is invisible because each station has its own files. TofuPilot's station comparison makes it obvious.

Reducing debug time for field returns. Customer reports a failure. Instead of guessing what happened during production, pull the unit's full test history in seconds. Every measurement, every step, every station.

Proving compliance with data. When auditors ask for test records, point them to TofuPilot. Structured, timestamped, immutable records replace binders full of printouts.