Cross-section Observation Apparatus End Point Detection
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Solution Overview
Problem
Automated cross-section processing and observation of semiconductor devices is challenging due to the changing shape of device patterns, which can lead to incorrect recognition of the end point in the processing process, especially when multiple observation images are combined to form a three-dimensional image.
Innovation Solution
A cross-section processing and observation apparatus that includes a sample stage, a focused ion beam column, and a charged particle detector, with a control portion that divides observation images into areas and finishes the process when a change is detected in only one area between successive images, ensuring accurate monitoring of the end point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic cross-section processing and observation is performed by monitoring the appearance of a desired observation target in observation images, then the process can be automated and productivity is improved, but the changing shape of device patterns in each observation image causes incorrect recognition of the end point and measurement precision deteriorates
Solution Approach 1:
The observation image is divided into multiple areas, and the control portion monitors image changes in each area separately. This segmentation allows the system to distinguish between changes caused by device pattern shape variations (which affect multiple areas) and changes indicating the appearance of a desired observation target (which affects only specific areas), thereby resolving the technical contradiction between automation and measurement precision.
2Manufacturing precision
If multiple observation images are acquired and combined to construct a high density three-dimensional image, then the quality of the three-dimensional image is improved, but the process complexity increases and requires automatic end point monitoring
Solution Approach 1:
By segmenting the observation image into multiple areas and monitoring changes in each area independently, the system simplifies the automatic end point monitoring process. The control portion can determine when to stop acquisition by checking whether changes occur in only one specific area, reducing the complexity of automatic processing while enabling high-quality three-dimensional image construction through multiple observations.
Solution Approach 2:
The control portion uses feedback from image change detection in divided areas to automatically control the acquisition process. When no image change is detected in a predetermined number of consecutive observation images, the control portion automatically terminates acquisition, providing a simple yet effective feedback mechanism that reduces system complexity while ensuring high-quality three-dimensional image construction.
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
Enables automatic and accurate cross-section processing and observation by correctly identifying the end point even when device patterns change in every observation image, ensuring precise exposure of the desired target in semiconductor devices.
Implementation Method 1
a focused ion beam column configured to irradiate the sample with a focused ion beam
Implementation Method 2
a charged particle detector configured to detect a charged particle emitted from the sample by irradiation of the focused ion beam
Data Source
AI summary
Provided is a cross-section processing and observation apparatus, including a control portion for repeatedly executing a process including slice processing by an ion beam and acquisition of a SIM image by a secondary electron emitted from a cross-section formed by the slice processing, in which the control portion divides an observation image into a plurality of areas, and finishes the process when a change has occurred between an image in one area of the plurality of areas and an image in an area, which corresponds to the one area, of an observation image of another cross-section acquired by the process.