TEM-Guided Wafer Thinning With Feedback Machining Control
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Solution Overview
Problem
The increasing miniaturization and complexity of semiconductor devices require higher levels of automation for the accurate preparation and observation of thin film samples using FIB-SEM devices and electron microscopes.
Innovation Solution
A semiconductor analysis system comprising a machining device for preparing thin film samples, a transmission electron microscope for acquiring images, and a host control device that evaluates samples, updates machining conditions, and improves the accuracy of sample preparation and observation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automation is increased for thin film sample preparation and observation, then productivity is improved, but manufacturing precision deteriorates due to the complexity of miniaturized semiconductor structures
Solution Approach 1:
The system implements a feedback mechanism where TEM images of prepared thin film samples are automatically evaluated by AI, and the results are used to update and optimize FIB machining conditions for subsequent sample preparations. This closed-loop feedback enables continuous improvement of preparation accuracy while maintaining high automation levels.
Solution Approach 2:
The system enables self-service through automated AI-based evaluation of TEM images and automatic generation of optimized machining conditions without human intervention. The host control device autonomously processes evaluation results and updates machining parameters, allowing the system to self-optimize while maintaining high productivity and precision.
2Productivity
If automation is increased for thin film sample observation, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system replaces manual observation and evaluation mechanisms with automated AI-based image analysis. The host control device automatically evaluates TEM images using AI algorithms, eliminating human subjectivity and variability while maintaining high measurement precision. This substitution enables both high productivity through automation and high precision through consistent AI-based evaluation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances the accuracy of automatic thin film sample preparation and observation, addressing the challenges posed by miniaturized and complex semiconductor structures.
Implementation Method 1
a transmission electron microscope device that acquires a transmission electron microscope image of the thin film sample
Implementation Method 2
a machining device that machines semiconductor wafer to prepare a thin film sample for observation
Data Source
AI summary
A semiconductor analysis system includes a machining device that machines semiconductor wafer to prepare a thin film sample for observation, a transmission electron microscope device that acquires a transmission electron microscope image of the thin film sample, and a host control device that controls the machining device and the transmission electron microscope device. The host control device evaluates the thin film sample based on the transmission electron microscope image, updates machining conditions based on an evaluation result of the thin film sample, and outputs the updated machining conditions to the machining device.