Adaptive Endpoint Detection for Semiconductor Delayering
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Solution Overview
Problem
The delayering process of semiconductor devices is challenging due to significant variations in monitor signals between different device classes, products, and sites, requiring significant operator input and hindering automation, especially in multi-shift production lines where skilled operators may not be available.
Innovation Solution
An automated system that uses an etcher with computer-readable media and hardware processors to perform adaptive endpoint detection, allowing for controlled delayering of successive layers with predetermined parameters, including beam etch and adaptive endpoint detection parameters, which can be adjusted dynamically based on monitoring signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated delayering is implemented with fixed parameters, then productivity increases, but manufacturing precision deteriorates due to signal variations between different device classes and sites
Solution Approach 1:
The system dynamically adjusts delayering parameters including beam current, power, focus diameter, and endpoint detection thresholds based on real-time monitor signals from each specific layer and device class. This allows the automated system to adapt to signal variations between different device classes, products, and sites while maintaining both high productivity and manufacturing precision.
Solution Approach 2:
The system changes multiple parameters simultaneously including beam etch parameters (beam current, power, focus diameter), endpoint detection parameters (thresholds, sensitivity), and scanning parameters (speed, pitch) to optimize delayering for each specific layer and device class. This multi-parameter adjustment enables the system to handle signal variations across different device types while maintaining automated operation.
2Manufacturing precision
If operator input is required for each delayering task, then manufacturing precision is maintained through expert judgment, but ease of operation deteriorates due to skill availability constraints
Solution Approach 1:
The system performs self-adjustment by automatically selecting appropriate parameter sets and adjusting delayering parameters based on real-time monitor signals from the specific device class and layer being processed. The automated endpoint detection and parameter adjustment eliminate the need for skilled operator intervention while maintaining high manufacturing precision through algorithmic control.
Solution Approach 2:
The system continuously monitors layer-specific signals during delayering and uses this feedback to dynamically adjust parameters and detect endpoints automatically. This closed-loop control enables the system to achieve expert-level precision without requiring skilled operators to be present on all shifts.
3Manufacturing precision
If distinct parameters are used for each layer and device class, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system uses a universal parameter management framework that can handle multiple device classes, layer types, and parameter sets through a single automated platform. The system automatically selects and manages the appropriate parameter set for each specific delayering task based on device class and layer identification, eliminating the need for manual parameter management while supporting distinct parameters for each layer and device type.
Solution Approach 2:
The system pre-configures parameter sets for different device classes and layer types before production begins. During automated operation, the system automatically selects the appropriate pre-configured parameter set based on the identified device class and layer, eliminating the complexity of real-time parameter management while maintaining layer-specific precision.
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 uniform and efficient delayering across various semiconductor devices and sites, reducing the need for operator intervention and improving process automation by accurately detecting endpoints and adjusting parameters for precise layer removal.
Implementation Method 1
The etcher can be configured to generate a focused ion beam (FIB)
Implementation Method 2
the operations can include receiving a signal indicating a measure of electrical current from the sample, with endpoint detection being based on the signal
Implementation Method 3
The system can also include a scanning electron microscope (SEM)
Data Source
AI summary
Adaptive endpoint detection is applied to delayering of a multi-layer sample utilizing a combination of dynamic and predetermined parameters. Tuned predetermined parameters, varying between layers of the sample, allow automated operation across multiple sites of a device. A semiconductor logic device is described, having a zone of thick metal layers and a zone of thin metal layers. The described techniques can be integrated with analysis operations and can be applied across a wide range of device types and manufacturing processes.