18326659. PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE
Organization Name
Inventor(s)
Dae Young Park of Suwon-si (KR)
Jeong Hoon Ko of Suwon-si (KR)
Seong Ryeol Kim of Suwon-si (KR)
Hyun Joong Kim of Suwon-si (KR)
PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18326659 titled 'PROCESS PROXIMITY EFFECT CORRECTION METHOD AND PROCESS PROXIMITY EFFECT CORRECTION DEVICE
Simplified Explanation
The patent application describes a method for improving the dispersion of patterns through process proximity effect correction using machine learning.
- Training a sensitivity model with layout images and critical dimensions of patterns.
- Estimating sensitivity prediction values for patterns.
- Determining correction rates for layout critical dimensions based on estimated sensitivity values.
Potential Applications
This technology can be applied in semiconductor manufacturing processes to enhance pattern dispersion and accuracy.
Problems Solved
1. Addressing proximity effect issues in pattern formation. 2. Improving the overall quality and precision of patterns in manufacturing processes.
Benefits
1. Enhanced pattern dispersion and accuracy. 2. Increased efficiency in manufacturing processes. 3. Reduction of errors and defects in pattern formation.
Original Abstract Submitted
Provided is a process proximity effect correction method capable of efficiently improving the dispersion of patterns. There is a process proximity effect correction method according to some embodiments, the process proximity effect correction method of a process proximity effect correction device for performing process proximity effect correction (PPC) of a plurality of patterns using a machine learning module executed by a processor, comprising: training a sensitivity model by inputting a layout image of the plurality of patterns and a layout critical dimension (CD) of the plurality of patterns into the machine learning module; estimating an after cleaning inspection critical dimension (ACI-CD) sensitivity prediction value of the plurality of patterns by inferring an ACI-CD prediction value of the plurality of patterns; and determining a correction rate of the layout CD of the plurality of patterns using the estimated sensitivity prediction value.
