Wafer Probe Mark Inspection for Faster WAT Setup

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

The existing wafer acceptance test (WAT) tools lack automated recognition and adjustment of probe marks, relying on manual inspection which is time-consuming and prone to inefficiency, affecting testing quality and capacity.

Innovation Solution

Integration of an AI-powered image inspection machine (IIM) for automated recognition and adjustment of probe marks, utilizing machine learning to analyze probe mark images and determine necessary adjustments, eliminating manual intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If manual visual inspection and adjustment of probe marks is used, then the WAT tool can operate with simpler structure, but the process time increases and productivity decreases

Engineering Contradiction:

Improvestructure simplicityVSAvoidprocess time

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent replaces the manual mechanical inspection and adjustment process with an automated optical imaging system coupled with machine learning algorithms. The image inspection machine captures probe mark images, and the machine learning model automatically identifies and evaluates probe mark quality, eliminating the need for manual visual inspection and significantly reducing process time while maintaining operational simplicity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If manual visual inspection and adjustment of probe marks is used, then the device structure remains simple, but quality assurance capability is reduced

Engineering Contradiction:

Improvestructure simplicityVSAvoidquality assurance

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent replaces manual quality assessment with an automated machine learning-based evaluation system. The image inspection machine captures probe mark images, and the trained machine learning model objectively evaluates probe mark quality based on learned patterns from diverse samples, providing consistent and reliable quality assurance without increasing structural complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements a feedback mechanism where the machine learning model's evaluation results are used to guide probe mark adjustments. The system provides quantitative feedback on probe mark quality, enabling automated iterative optimization of probe positioning and pressure parameters to achieve consistent high-quality probe marks.

Inventive Principle:

Principle #23Feedback

3Productivity

If automated image inspection machine is integrated, then productivity and quality assurance are improved, but device complexity increases

Engineering Contradiction:

Improveprocess timeVSAvoidstructure complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent integrates the image inspection machine and machine learning evaluation system into the existing WAT tool platform, allowing the same system to perform both wafer parameter testing and probe mark quality assessment. This multi-functional integration improves productivity and quality assurance while minimizing the increase in device complexity by sharing hardware and software resources.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If automated image inspection machine is integrated, then quality assurance and automation level are improved, but manual operation ease is reduced

Engineering Contradiction:

Improvequality assuranceVSAvoidmanual intervention

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent implements a self-service system where the machine learning model automatically evaluates probe mark quality and generates adjustment recommendations without requiring manual intervention. The system autonomously iterates through probe parameter adjustments based on machine learning guidance, eliminating the need for operators to perform manual visual inspection and adjustment while maintaining high quality assurance standards.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12613270B2Wafer acceptance test tool and test method using thereof

Publication Date: 2026.04.28 TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD

AI summary

A method includes pressing probes against probe pads on a test line over a substrate at a first position on the probe pads to form first probe marks on the probe pads; capturing first images of the first probe marks on the probe pads; transmitting the captured first images of the first probe marks to an image inspection machine; identifying the first probe marks from the probe pads of the captured first images through the image inspection machine; determining whether the identified first probe marks are acceptable through the image inspection machine; in response to the determination determines that the identified first probe marks are acceptable, performing a first wafer acceptance test (WAT) to the substrate with the probes at the first position.