At Teledyne DALSA, I worked as a Sensor Technologist, focusing on the reliability and performance of space satellite image sensors. This role involved hands on hardware testing, defect analysis, and data-driven decision-making to ensure the sensors met stringent quality standards for use in critical space applications. Below, I’ll walk you through the technical challenges, solutions, and methodologies I employed, demonstrating how this experience aligns with the responsibilities of a hardware engineer in general.
At Teledyne, we have an ISO 4 cleanroom for performing inspections and tests on our sensors as they are highly sensitive, here I inspected our space satellite image sensors for defects while strictly adhering to environment guidelines. I used by using various electronic test instruments such as oscilloscopes and multimeters for electrical testing, high intensity lights and our microscope for visual inspection and imagery. Sensors are tested under controlled conditions to ensure consistent pixel readout and adherence to expected parameters. Images of the sensors are taken to be added to the database and assessed by the team.
Imaging: Brightfield, darkfield, and polarized light techniques were used in microscope inspection to detect physical surface defects, particle contamination, and bonding irregularities.
Electrical testing: Oscilloscopes verify signal integrity, response times (including rise and fall times), and noise levels, while multimeters check continuity, resistance, and potential shorts or opens in the sensor circuits which were found to be common.
I also developed custom scripts in MATLAB and LabVIEW to automate some of these testing procedures.
Technical Implementation: Developed a LabVIEW program to measure voltage spikes and signal integrity:
Acquire Waveform → Filter Noise → Detect Peaks → Compare to Threshold → Log Results
Used MATLAB to analyze sensor images and detect anomalies:
% MATLAB script for image analysis
img = imread('sensor_image.png');
img_gray = rgb2gray(img);
edges = edge(img_gray, 'Canny');
imshow(edges);Impact: Reduced manual testing time by 30% through automation, noticably improved the accuracy of defect detection.
I managed a centralized database to catalog sensor defects and their characteristics, enabling the engineering team to identify patterns and trends in failure modes. This data became a critical tool for root cause analysis and quality improvement.
Technical Implementation: Used SQL for database queries, ensuring efficient workflow for root cause analysis. Developed a Python script to automate the ingestion of sensor test data into the database:
import pandas as pd
import sqlite3
# Load sensor test data
data = pd.read_csv("sensor_test_data.csv")
# Connect to SQLite database
conn = sqlite3.connect("sensor_defects.db")
data.to_sql("defects", conn, if_exists="replace", index=False)Impact: Reduced the time required to identify and categorize defects. Enabled the team to prioritize high-impact failure modes for further investigation.
I conducted root cause analysis for sensor defects, producing detailed technical reports that informed design improvements and manufacturing processes.
Methodology: Used statistical methods like logistic regression to predict key metrics (e.g., voltage spikes, signal-to-noise ratio) and identify correlations between sensor performance and defect types. Example of logistic regression using SciPy:
from scipy.stats import linregress
import pandas as pd
# Load sensor data
data = pd.read_csv("sensor_data.csv")
# Perform logistic regression
slope, intercept, r_value, p_value, std_err = linregress(data["voltage_spike"], data["defect_probability"])
print(f"Slope: {slope}, Intercept: {intercept}, R-squared: {r_value**2}")
Impact: Identified the root cause of a recurring defect in voltage regulation circuits, leading to a design change that reduced defect rates by 25%.
Challenge: Sensor data was often noisy and difficult to interpret.
Solution: I implemented signal processing techniques (e.g., noise filtering, edge detection) to improve data quality and enable accurate defect detection.
Challenge: Manual testing processes were prone to human error.
Solution: I developed automated test scripts in MATLAB and LabVIEW, ensuring consistent and repeatable results.
Challenge: Some defects occurred intermittently, making them difficult to reproduce and diagnose.
Solution: I used statistical methods to identify patterns in defect occurrence, enabling the team to address underlying causes.
Improved Reliability: My work contributed to a 25% reduction in defect rates for space satellite image sensors.
Enhanced Efficiency: Automated testing and data analysis reduced manual effort and improved testing accuracy.
Data-Driven Attitiude: My use of statistical methods and data visualization tools increased momentum for data-driven decision-making within the team.
This experience demonstrates my ability in hardware reliability engineering, and develop innovative testing methodologies.