AI-Driven Semiconductor Process Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The semiconductor fabrication process is cumbersome and time-consuming due to the need for extensive validation and certification of process changes, which often require weeks of computational modeling and resource-intensive simulations, leading to delays and inefficiencies in implementing process enhancements.
Innovation Solution
The implementation of an artificial intelligence system that uses machine learning to predict and validate semiconductor device parameters, reducing the need for lengthy simulations by training a predictive AI to accelerate the fabrication process and identify potential violation conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer-based modeling is used to validate process changes, then measurement precision and reliability are improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The patent creates a digital twin (virtual model) of the semiconductor fabrication process that replicates the behavior of the physical system. This virtual model can be simulated and validated without affecting the actual production process, enabling rapid iteration and validation of process changes in silico before physical implementation, thereby dramatically reducing validation time while maintaining accuracy
Solution Approach 2:
The patent performs virtual validation and simulation of process changes before physical implementation. By conducting preliminary testing in the digital twin environment, potential issues are identified and resolved beforehand, preventing costly rework and delays during actual fabrication while ensuring measurement precision is maintained
2Manufacturing precision
If extensive computer-based modeling is performed to ensure manufacturing precision, then manufacturing precision is improved, but productivity and speed of implementation worsen
Solution Approach 1:
The patent replaces physical trial-and-error fabrication with virtual simulation and modeling. Instead of physically testing process changes which consumes time and resources, the system uses computational models to predict outcomes, substituting mechanical/physical processes with information-based processes that can be executed rapidly while maintaining manufacturing precision
Solution Approach 2:
The patent enables rapid adjustment and testing of process parameters in the virtual model without physical constraints. Process variables can be changed instantaneously in simulation to explore different scenarios, optimize parameters, and validate changes before committing to physical fabrication, thereby improving both precision and productivity
3Reliability
If traditional validation methods are used to ensure reliability of process changes, then reliability is improved, but device complexity and resource requirements worsen
Solution Approach 1:
The patent creates a universal digital twin platform that can validate multiple different process changes across various semiconductor fabrication processes using the same virtual infrastructure. This multi-functional system reduces overall complexity by providing a standardized validation approach rather than requiring separate validation systems for each process type, while maintaining high reliability through consistent virtual testing protocols
Data Source
AI summary
Fabricating a first semiconductor device cell using a first process based on a first process parameter or material comprises extracting semiconductor device parameters from the first process parameters to obtain extracted semiconductor device parameters of a first semiconductor device cell. The fabrication process includes training an artificial intelligence to obtain a predictive artificial intelligence using training data as input, the training data comprising the extracted semiconductor device cell parameters and the first process parameter or material. A proposed process modification is provided to the predictive artificial intelligence to generate a predicted cell delay by the predictive artificial intelligence. The predicted cell delay is evaluated against a cell delay threshold. When the predicted cell delay satisfies the cell delay threshold, a new semiconductor device cell is fabricated using a modified process incorporating the proposed process modification.