Reducing Semiconductor Defects with Probabilistic Contrastive Counterfactuals

Reducing Semiconductor Defects with Probabilistic Contrastive Counterfactuals
Semiconductor ManufacturingDefect DetectionCausal AICounterfactual AnalysisAutomationAI in Manufacturing

Introduction

In modern semiconductor manufacturing, the tiniest error—a microscopic misalignment or a slight deviation in dose—can lead to significant defects, higher costs, and lower yield. Ensuring a near-flawless manufacturing process is a top priority for chipmakers worldwide. However, as semiconductor fabrication processes grow more complex, traditional defect detection methods often fall short.

This is where ThirdAI Automation’s innovative approach steps in, revolutionizing how fabs not only detect defects but also understand why they occur. Our latest research on this cutting-edge methodology was recently presented at SPIE Advanced Lithography & Patterning 2025. Read the full paper here.

Why Traditional Defect Detection Falls Short?

Semiconductor fabrication involves multiple intricate stages—lithography, etching, deposition, testing, and more—each with its own process parameters (such as temperature, pressure, and material dose). Even small variations can lead to significant defects. Conventional defect detection methods typically rely on:

  • Fixed rule-based systems (e.g., “flag anything above X measurement as defective”).

  • Complex machine learning models that detect anomalies but act as black boxes, leaving engineers with no clear insights into defect causality.

Both approaches leave engineers asking:

  • Why did this defect occur?
  • What if we adjust a process parameter—will it prevent future defects?

Without transparent, actionable insights, preventing defects at scale becomes a guessing game.

What Are Probabilistic Contrastive Counterfactuals?

The heart of our AI-powered framework lies in generating probabilistic contrastive counterfactuals—powerful “what if” scenarios that provide engineers with precise insights into defect causality.

For example, if a wafer is defective due to excessive alignment offset, a traditional system would simply state:

🛑 “Defective wafer detected.”

In contrast, a counterfactual analysis provides a deeper explanation:

✅ “If we reduced the alignment offset from 5 nm to 2 nm, the probability of producing a non-defective wafer would increase from 30% to 85%.”

This approach delivers two key benefits:

  • Contrastive insight: It highlights the difference between the current state (5 nm) and an improved scenario (2 nm).

  • Probabilistic reasoning: Engineers can quantify the likelihood of success if process parameters are adjusted, leading to more informed decision-making.

How Does It Help Semiconductor Manufacturing?

By leveraging causal AI and counterfactual reasoning, fabs gain:

  • Root Cause Identification – Engineers no longer rely on vague suspicions but get precise insights into which process parameters (e.g., gas pressure, etch time) directly impact defect occurrence.

  • Necessity & Sufficiency Testing – The system determines whether a parameter is necessary for defects to appear and whether adjusting it is sufficient to prevent them.

  • A Global and Local View – Counterfactuals provide both a big-picture overview of how factors influence all wafers and a microscopic analysis of why a specific wafer failed.

SPIE-Reverse-Pyramid

Building Trust with Actionable Insights

For semiconductor engineers, trust in AI-driven tools is paramount. Traditional black-box models often declare a wafer as “defective” without providing any insights into the cause or possible solutions.

Probabilistic contrastive counterfactuals bridge this gap by:

  • Highlighting the exact process changes needed to reduce defects.
  • Providing a clear probability of success for different process adjustments.
  • Helping fabs tweak only the necessary parameters, saving time and resources while maximizing yield.

Looking Ahead

As semiconductor manufacturing advances and defect prevention becomes even more critical, AI-powered tools that detect, explain, and optimize will be essential. ThirdAI Automation is at the forefront of this shift, combining causal reasoning with explainable AI-driven “what if” scenarios to improve chip yields and reduce costly errors.

For a deeper dive into the methodology behind probabilistic contrastive counterfactuals, check out our SPIE Advanced Lithography & Patterning 2025 presentation. Read more.

👉 In our next release, we’ll explore how causal reasoning techniques can be applied to predictive maintenance! Stay tuned!