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[Model-based FTIR reflectometry measurement system for deep trench structures of DRAM]
Shi-yuan Liu1, Chuan-wei Zhang, Hong-wei Shen
1State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China. shyliu@mail.hust.edu.cn
A new Fourier Transform Infrared (FTIR) reflectometry method accurately measures deep trench structures in dynamic random access memory (DRAM). This non-contact, high-resolution technique enhances signal quality for precise geometric parameter extraction.
Area of Science:
- Optics and Photonics
- Materials Science
- Semiconductor Device Physics
Context:
- Accurate measurement of deep trench structures is critical for advanced semiconductor manufacturing, particularly for dynamic random access memory (DRAM) devices.
- Existing metrology techniques may face limitations in resolution, cost, or throughput for nanoscale features.
- Fourier Transform Infrared (FTIR) reflectometry offers a potential non-destructive approach for in-situ process monitoring.
Purpose:
- To propose and validate a novel method and system for measuring deep trench structures in DRAM using FTIR reflectometry.
- To enhance the signal-to-noise ratio (SNR) of FTIR measurements by optimizing optical parameters.
- To demonstrate the capability of extracting trench geometric parameters with nanometer-scale accuracy.
Summary:
- A measurement system based on FTIR reflectometry is developed for deep trench structures.
- Key innovations include regulating slit aperture and optimizing incidence angle to suppress backside reflection and improve SNR.
- Experimental results confirm nanometer-scale accuracy in extracting trench geometric parameters, validating the system's effectiveness.
Impact:
- Provides a non-contact, non-destructive, time-effective, and low-cost metrology tool for high-resolution measurement of deep trench structures.
- Enables precise on-line monitoring and process control in microelectronics and microelectromechanical system (MEMS) manufacturing.
- Potential to improve yield and reduce costs in semiconductor fabrication processes.
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