Etch Endpoint Detection via Optical Signal Filtering
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Solution Overview
Problem
High performance plasma-assisted etch processes in semiconductor manufacturing are challenging due to difficulties in monitoring, controlling, and optimizing etch processes, particularly in situ monitoring and etch endpoint detection, with existing computational models lacking accuracy and reliability.
Innovation Solution
A method and system that measure optical signals from etched features on a substrate, apply these signals to a computational model to predict target geometric parameters such as etch depth, pitch, or critical dimension, and adjust the etch process until the desired endpoint is reached, using a controller with instructions for measuring, providing, and applying optical signals to predict and compare geometric parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If computational models are used to simulate physical-chemical processes during etch processes, then process monitoring and control capability is improved, but model accuracy and reliability remain insufficient
Solution Approach 1:
The patent replaces traditional mechanical/optical endpoint detection methods with a computational model-based approach. The model uses process parameters (power, pressure, temperature, composition) to predict geometric parameters (etch depth, critical dimension, pitch) and determines endpoint automatically, substituting physical measurement systems with a virtual simulation system that provides more reliable and accurate process monitoring.
2Measurement precision
If traditional endpoint detection methods are used, then device complexity is low, but measurement precision and reliability are insufficient
Solution Approach 1:
The computational model serves multiple functions simultaneously: it predicts etch depth, critical dimension, pitch, and endpoint detection. This multi-functional approach improves measurement precision across multiple parameters without requiring separate detection systems for each parameter, thereby managing device complexity through a unified modeling framework.
Solution Approach 2:
The patent introduces computational models as an intermediary between process control parameters and endpoint detection. Rather than directly measuring physical properties with complex sensors, the model acts as a mediator that translates easily measurable process parameters (power, pressure, temperature, composition) into accurate predictions of geometric parameters and endpoint conditions.
3Manufacturing precision
If process engineers manually test etch process parameters, then manufacturing precision can be optimized, but productivity and time efficiency deteriorate
Solution Approach 1:
The computational model performs preliminary calculations and predictions before actual etching occurs. By pre-determining the relationship between process parameters and geometric outcomes through modeling, the system guides process optimization without requiring extensive manual trial-and-error testing, thereby maintaining manufacturing precision while significantly improving productivity.
Solution Approach 2:
The system implements continuous feedback by comparing real-time process parameters against the computational model predictions. This feedback mechanism automatically adjusts process parameters to achieve target geometric parameters, maintaining high manufacturing precision while reducing the time required for process optimization compared to manual testing approaches.
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
This approach enables real-time monitoring and precise control of etch processes, improving accuracy and reliability by terminating the etch process when target geometric parameters are reached, thus enhancing the efficiency and effectiveness of semiconductor manufacturing.
Implementation Method 1
measuring optical signals produced by optical energy interacting with features being etched on the substrate
Data Source
AI summary
Monitoring a geometric parameter value for one or more features produced on a substrate during an etch process may involve: (a) measuring optical signals produced by optical energy interacting with features being etched on the substrate; (b) providing a subset of the measured optical signals, wherein the subset is defined by a range where optical signals were determined to correlate with target geometric parameter values for features; (c) applying the subset of optical signals to a model configured to predict the target geometric parameter values from the measured optical signals; (d) determining, from the model, a current value of the target geometric parameter of the features being etched; (e) comparing the current value of the target geometric parameter of the features being etched to an etch process endpoint value for the target geometric parameter; and (f) repeating (a)-(e) until the comparing in (e) indicates that the current value of the target geometric parameter of the features being etched has reached the endpoint value.