CMP Pressure Control Using Constrained Cost Function
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Solution Overview
Problem
Chemical mechanical polishing (CMP) processes face challenges in achieving uniform material removal rates due to variations in substrate thickness, slurry composition, polishing pad conditions, and pressure loads, leading to within-wafer non-uniformity and edge exclusion issues.
Innovation Solution
A computer program or method that uses in-situ monitoring to calculate adjustments for polishing parameters by minimizing a cost function, considering differences between current and target characterizing values, and projected pressure changes, while adhering to constraints, to optimize pressure control across multiple zones on a substrate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional CMP control methods are used, then the polishing process is simple to operate, but within-wafer non-uniformity and edge exclusion occur due to variations in material removal rate
Solution Approach 1:
The substrate surface is divided into multiple zones (e.g., center, mid-radius, edge zones) with independently controllable pressure chambers. This segmentation allows differential pressure control across different regions to compensate for variations in material removal rate and achieve uniform polishing across the wafer surface.
Solution Approach 2:
The system dynamically adjusts pressure distribution in real-time during the polishing process based on feedback from in-situ monitoring. The controller continuously modifies pressure chamber outputs to maintain target polishing rates across different zones, transforming static pressure control into a dynamic adaptive system.
2Productivity
If pressure is increased to accelerate material removal, then productivity improves, but pressure changes cause instability and within-wafer non-uniformity
Solution Approach 1:
Different pressure levels are applied to different zones based on their specific polishing requirements. Zones with lower material removal rates receive higher pressure, while zones with higher removal rates receive lower pressure, creating a localized quality approach that maintains stability while achieving overall productivity goals.
Solution Approach 2:
In-situ monitoring systems continuously measure polishing progress and provide feedback to the controller. The controller uses this feedback to adjust pressure in real-time, preventing instability caused by excessive pressure changes and ensuring reliable, consistent polishing results.
3Manufacturing precision
If in-situ monitoring and real-time control are implemented, then polishing precision and uniformity improve, but system complexity and computational requirements increase
Solution Approach 1:
The system pre-calculates pressure adjustments based on predicted polishing outcomes and applies them proactively. By anticipating future polishing states and preparing control actions in advance, the system achieves high precision without requiring excessively complex real-time computation during the polishing process.
Solution Approach 2:
The system monitors and adjusts key polishing parameters (pressure, speed, slurry flow) in real-time based on measured thickness variations. By dynamically changing these parameters according to actual process conditions, the system achieves precise thickness control while managing complexity through focused parameter optimization.
Data Source
AI summary
Controlling a polishing system includes receiving from an in-situ monitoring system, for each region of a plurality of regions on a substrate being processed by the polishing system, a sequence of characterizing values for the region. For each region, a polishing rate is determined for the region, and an adjustment is calculated for at least one processing parameter. Calculation of the adjustment includes minimizing a cost function that includes, for each region, a difference between a current characterizing value or an expected characterizing value at an expected endpoint time and a target characterizing value for the region, and optimization of the cost function is subject to at least one constraint.