Semiconductor Deposition Recipe Control Using Wafer Feedback
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Solution Overview
Problem
Challenges arise during the scaling-down process of semiconductor devices in achieving improved quality, yield, performance, and reliability while reducing complexity, particularly in the fabrication processes.
Innovation Solution
A fabrication system incorporating an artificial intelligence module that analyzes data from measurement modules to update etching, deposition, and implantation recipes on a wafer-to-wafer basis, ensuring parameters are within predetermined ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If semiconductor device dimensions are scaled down continuously, then computing ability is improved, but fabrication quality and yield deteriorate due to various process issues
Solution Approach 1:
The patent implements a feedback mechanism where measurement modules collect data from processed wafers, the AI module analyzes this data to determine if parameters are within predetermined ranges, and the system automatically updates recipes for subsequent wafers. This closed-loop feedback system enables real-time process correction and compensation for scaling-related manufacturing challenges.
Solution Approach 2:
The patent introduces dynamic adaptability through AI-driven recipe updates that adjust process parameters in real-time based on measured wafer states. Instead of static fabrication processes, the system dynamically modifies etching, deposition, and implantation recipes to compensate for variations introduced by device scaling, thereby maintaining manufacturing precision despite continuous dimension reduction.
2Speed
If semiconductor device dimensions are scaled down continuously, then computing ability is improved, but process complexity increases
Solution Approach 1:
The patent implements a self-service system where the fabrication process automatically monitors its own output through measurement modules, analyzes the data through AI algorithms, and self-corrects by updating recipes without external intervention. This automation reduces the complexity burden on operators and streamlines the fabrication process despite the underlying physical complexities of scaling.
Solution Approach 2:
The patent systematically manages process complexity by dynamically adjusting multiple process parameters (temperature, pressure, gas flow rates, power levels) based on AI analysis of wafer measurements. The AI module correlates measurements with process parameters to identify deviations and automatically modifies parameters to compensate, transforming complex scaling challenges into manageable parameter adjustments.
3Reliability
If AI module dynamically updates recipes based on real-time feedback, then yield and reliability are improved, but system complexity increases
Solution Approach 1:
The patent employs a universal AI module that handles multiple fabrication processes (etching, deposition, implantation) through a single integrated system. The same AI architecture and decision-making framework are applied across different process types, reducing the need for separate control systems for each process and thereby limiting the increase in overall system complexity while achieving improved yield and reliability.
Solution Approach 2:
The patent introduces an intermediary AI module that acts as a mediator between measurement data and process control. Rather than directly connecting measurement modules to individual process equipment, the AI module serves as a central intermediary that processes data, makes decisions, and coordinates updates across multiple processes. This intermediary approach simplifies the overall system architecture compared to direct point-to-point control connections.
Data Source
AI summary
The present application discloses a deposition system. The deposition system includes a deposition module executing a first deposition recipe on a first wafer to turn a first wafer state of the first wafer to a second wafer state; a first measurement module collecting the second wafer state of the first wafer to generate a first set of data; and an artificial intelligence module coupled to the first measurement module and the etch module, analyzing the first set of data and update the first deposition recipe to a second deposition recipe when the first set of data is not within a predetermined range. The artificial intelligence module is configured for generating the second deposition recipe taking into consideration at least one of a deposition rate of the second wafer, a rate of rotation of the second wafer, and a tilt angle of the second wafer.