test-automation

Automated test equipment control and data acquisition skill for hardware validation, with VISA/SCPI instrument communication, test sequence scriptin…

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技能内容

Test Equipment Automation Skill

Automated test equipment control and data acquisition for hardware validation and characterization.

Purpose

This skill provides comprehensive capabilities for automating electronic test equipment, enabling consistent and repeatable hardware validation workflows. It supports instrument communication, automated test sequences, data logging, and analysis.

Capabilities

Instrument Communication

VISA/SCPI instrument communication protocols
Support for GPIB, USB-TMC, LAN/VXI-11, and serial interfaces
Instrument discovery and resource management
Error handling and timeout management
Connection pooling for multi-instrument setups

Oscilloscope Automation

Keysight, Tektronix, Rohde & Schwarz oscilloscope control
Channel configuration and coupling
Trigger setup (edge, pulse, pattern, serial)
Waveform acquisition and transfer
Measurement extraction (rise time, frequency, duty cycle)
Screenshot capture and documentation

Spectrum Analyzer Control

Frequency span and center frequency configuration
Resolution and video bandwidth settings
Marker operations and peak search
Trace capture and data export
Limit line testing and pass/fail determination
EMC pre-compliance measurements

Power Supply and Electronic Load Control

Output voltage and current programming
Protection limit configuration
Sequencing and timing control
Load transient generation
Efficiency measurement automation

DMM and SMU Automation

DC/AC voltage and current measurements
Resistance and continuity testing
Source-measure unit (SMU) operations
IV curve characterization
Temperature coefficient measurements

Data Logging and Analysis

Real-time data acquisition
Statistical analysis (mean, std, min, max)
Data logging to CSV, JSON, HDF5
Time-series trending
Alarm and limit monitoring

Test Sequence Scripting

Sequential and parallel test execution
Conditional branching based on results
Loop and iteration support
Test fixture abstraction
Parameterized test cases

Measurement Uncertainty Analysis

Type A (statistical) uncertainty evaluation
Type B (systematic) uncertainty estimation
Combined uncertainty calculation
Coverage factor and confidence intervals
GUM-compliant uncertainty budgets

Report Generation

Automated test report creation
Pass/fail summary tables
Measurement data visualization
Traceability documentation
Export to PDF, HTML, Excel formats

Prerequisites

Installation

pip install pyvisa pyvisa-py numpy pandas matplotlib

Optional Dependencies

# For HDF5 data storage
pip install h5py tables

# For advanced analysis
pip install scipy uncertainties

# For report generation
pip install jinja2 weasyprint

Usage Patterns

Basic Instrument Communication

import pyvisa

# Initialize resource manager
rm = pyvisa.ResourceManager()

# List available instruments
print(rm.list_resources())

# Connect to oscilloscope
scope = rm.open_resource('TCPIP::192.168.1.100::INSTR')
scope.timeout = 5000  # 5 second timeout

# Query identification
idn = scope.query('*IDN?')
print(f"Connected to: {idn}")

# Configure and measure
scope.write(':CHANnel1:DISPlay ON')
scope.write(':CHANnel1:SCALe 1.0')  # 1V/div
scope.write(':TIMebase:SCALe 0.001')  # 1ms/div
scope.write(':TRIGger:EDGE:SOURce CHANnel1')
scope.write(':TRIGger:EDGE:LEVel 0.5')

# Read measurement
vpp = float(scope.query(':MEASure:VPP? CHANnel1'))
freq = float(scope.query(':MEASure:FREQuency? CHANnel1'))

print(f"Vpp: {vpp:.3f} V, Frequency: {freq:.2f} Hz")

scope.close()

Automated Test Sequence

from dataclasses import dataclass
from typing import List, Dict, Any
import time

@dataclass
class TestResult:
    name: str
    passed: bool
    value: float
    unit: str
    limit_low: float
    limit_high: float

class TestSequence:
    def __init__(self, instruments: Dict[str, Any]):
        self.instruments = instruments
        self.results: List[TestResult] = []

    def run_test(self, name: str, measure_func, limit_low: float,
                 limit_high: float, unit: str) -> TestResult:
        value = measure_func()
        passed = limit_low <= value <= limit_high
        result = TestResult(name, passed, value, unit, limit_low, limit_high)
        self.results.append(result)
        return result

    def generate_report(self) -> Dict:
        return {
            'total_tests': len(self.results),
            'passed': sum(1 for r in self.results if r.passed),
            'failed': sum(1 for r in self.results if not r.passed),
            'results': [vars(r) for r in self.results]
        }

# Example usage
def measure_output_voltage():
    return float(dmm.query(':MEASure:VOLTage:DC?'))

sequence = TestSequence({'dmm': dmm, 'psu': psu})
sequence.run_test('Output Voltage', measure_output_voltage, 4.9, 5.1, 'V')

Measurement Uncertainty Analysis

from uncertainties import ufloat
import numpy as np

class UncertaintyAnalysis:
    def __init__(self):
        self.measurements = []
        self.instrument_uncertainty = 0.0

    def add_measurement(self, value: float):
        self.measurements.append(value)

    def set_instrument_uncertainty(self, uncertainty: float):
        """Set Type B uncertainty from instrument specifications"""
        self.instrument_uncertainty = uncertainty

    def calculate_combined_uncertainty(self, coverage_factor: float = 2.0):
        # Type A uncertainty (statistical)
        n = len(self.measurements)
        mean = np.mean(self.measurements)
        std = np.std(self.measurements, ddof=1)
        type_a = std / np.sqrt(n)

        # Type B uncertainty
        type_b = self.instrument_uncertainty / np.sqrt(3)  # Rectangular distribution

        # Combined standard uncertainty
        combined = np.sqrt(type_a**2 + type_b**2)

        # Expanded uncertainty
        expanded = coverage_factor * combined

        return {
            'mean': mean,
            'type_a_uncertainty': type_a,
            'type_b_uncertainty': type_b,
            'combined_uncertainty': combined,
            'expanded_uncertainty': expanded,
            'coverage_factor': coverage_factor
        }

# Usage
analysis = UncertaintyAnalysis()
for _ in range(10):
    analysis.add_measurement(float(dmm.query(':MEASure:VOLTage:DC?')))
analysis.set_instrument_uncertainty(0.001)  # 1mV from spec sheet
result = analysis.calculate_combined_uncertainty()
print(f"Voltage: {result['mean']:.4f} +/- {result['expanded_uncertainty']:.4f} V (k=2)")