Ion Implantation Process Control via Reinforcement Learning
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Solution Overview
Problem
Current ion implantation methods face challenges in achieving a uniform ion depth profile across a wafer, requiring multiple implantation processes and extended time to match a target box profile, which complicates the production of consistent semiconductor devices.
Innovation Solution
The implementation of reinforcement learning to adjust ion energy, angle of incidence, and vertical movement speed, using a Deep Neural Network algorithm to optimize process conditions and generate a process recipe that ensures an ion depth profile matching a box profile in a single ion implantation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple ion implantation processes are used to achieve a uniform ion depth profile, then manufacturing precision is improved, but production time increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting ion energy, angle of incidence, and vertical movement speed during a single ion implantation process. These parameter variations enable the system to achieve a uniform ion depth profile (box profile) without requiring multiple sequential implantation processes, thereby reducing production time while maintaining manufacturing precision.
Solution Approach 2:
The patent implements dynamics by using reinforcement learning to continuously optimize process conditions during the ion implantation process. The system dynamically adjusts parameters based on real-time feedback and learned patterns, enabling a single process to achieve what traditionally required multiple static processes, thus resolving the time-precision contradiction.
2Productivity
If reinforcement learning is used to optimize ion implantation process conditions, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent applies feedback through reinforcement learning, where the control system continuously monitors the ion depth profile and adjusts process conditions based on the difference between actual and target profiles. This feedback mechanism enables the system to achieve high productivity with a single optimized process, while the complexity is managed through iterative learning rather than complex hardware modifications.
Solution Approach 2:
The patent replaces traditional mechanical control approaches with an intelligent control system based on reinforcement learning and deep neural networks. This substitution reduces the need for complex mechanical adjustments and multiple physical processes, achieving high productivity through software-based optimization while managing system complexity through algorithmic efficiency.
3Loss of time
If a single ion implantation process is used, then production time is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The patent applies preliminary action by using reinforcement learning to pre-optimize the ion implantation process conditions before actual production. The deep neural network learns the optimal parameter combinations and process conditions in advance, enabling a single implantation process to achieve uniform ion depth profiles without requiring multiple corrective processes, thus maintaining precision while reducing time.
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 significantly reduces the time required for ion implantation processes while maintaining consistent ion concentration, enabling the production of semiconductor devices with improved performance by achieving a uniform ion depth profile in a single step.
Implementation Method 1
An ion source gas is ionized
Implementation Method 2
required ions are selected and accelerated to form an ion beam. A desired amount of the ion beam is implanted into a surface of a wafer
Implementation Method 3
Ion energy may be determined by a potential difference applied between an ion beam apparatus and a wafer
Data Source
AI summary
An ion depth profile control method includes performing reinforcement learning, whereby a similarity between an ion depth profile and a box profile is output as a reward when the similarity is equal to or greater than a set criterion, the ion depth profile being an ion concentration according to a wafer depth in an ion implantation process, and the box profile being a target profile, obtaining at least one process condition of the ion implantation process as a result of the reinforcement learning, and generating a process recipe regarding the at least one process condition.