How Test Engineers Decide What Counts as Good Enough

Gideon Lewis-Kraus reviews “Kobuk the Destroyer, and Other Tales from the Wild, Unseen World of Test Engineering,” by Alex Davies.

Beyond Conventional Ring Oscillators: Purpose-Built Process Detectors For Deeper Silicon Insight

How design-aware monitor analytics connects production measurements with device behavior and pre-silicon expectations.

Smart Outlier Detection

Just because a chip is deemed good doesn't mean it will work as expected.

Design for Testability (DFT): How Do We Know a Chip Actually Works?

Designing a semiconductor chip is not only about making it perform its intended function.

Flat Enough? Warpage Management Gets Harder In Advanced Packaging

As packages get larger and thinner, warpage is becoming a process-control problem rather than a simple flatness spec.

In Praise of Observational Evidence

Everyone knows RCTs are the gold standard of evidence. ….Right?

It is a rare randomized trial (studying a particular intervention with a sufficiently large and well-distributed sample population) that can detect an 0.3% difference in a binary random variable — but Laplace could, more than 200 years ago.

You can be relatively sure that a p-value in one clinical trial conveys the same information as it does in another clinical trial, at least as far as the mathematical model is concerned.

Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing) - MarkTechPost

Safely Deploying Machine Learning Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)

Note: This bookmark, **[Safely Deploying ML Models to Production: Four Controlled Strategies](#rid:1652757915)**, is an article from MarkTechPost that outlines how to release machine learning models into production while minimizing risk. The article details four key deployment strategies: * **A/B Testing**: Randomly splits a small percentage of total traffic to the new model while the rest stays on the…

Rethinking Scan Chains In Semiconductor Test

Finding defects now requires multiple data types, and a much more thorough search.

Mastering Statistical Tests

Your Guide to Choosing the Right Test for Your Data

Mock vs Stub vs Fake: Understand the difference

Introduction Testing software is like putting it through a series of challenges to make...

12 Ways to Test Your Forecasts like A Pro

How to find the best performance estimation approach for time-series forecasts among 12 strategies proposed in the literature. With Python…

Essential Tools for a Successful DevOps Engineer

Introduction: The software development process has been transformed by the DevOps...

Write Readable Tests for Your Machine Learning Models with Behave

Use natural language to test the behavior of your ML models

We need to talk about fixtures

How the principle of connascence will help us to move away from fixtures.

Writing better user stories with Gherkin and Cucumber

Writing automated unit tests for the software we build can seem like a large amount of groundwork without a clear payoff. However, the…

Introduction to Front-End unit testing

An introduction meant for people who have never delved into unit testing

Why Data Format Slows Chip Manufacturing Progress

Adoption of new format will take time, but it also will add consistency into data as volume grows.

What’s WAT? An Overview Of WAT/PCM Data?

More about the data that the fab makes available to the fabless customer when the wafer is ready to ship.

Machine Learning Enabled High-Sigma Verification Of Memory Designs

Variation-aware memory verification with brute force Monte Carlo accuracy in much less time.

Even Better Specs

Guidelines for Maintainable Tests