Predictive 3D Multi-Planar Semiconductor Shape Optimization
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Solution Overview
Problem
Current manufacturing methods for 3D integrated circuits are inefficient in predicting and optimizing multi-planar shapes for semiconductor chips, failing to effectively meet specific user needs, particularly in achieving optimal area utilization for advanced processes like the 3 nm node.
Innovation Solution
A predictive manufacturing system utilizing machine learning and artificial intelligence to analyze and determine the best 3D multi-planar shape by running simulations on multiple shapes, applying geometric deep learning and Convolutional Neural Networks to process geometrical and electrical characteristics, and optimizing surface area yield based on design and manufacturing process rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current manufacturing methods are used for 3D integrated circuits, then production can proceed with existing processes, but manufacturing efficiency is low and optimal shape prediction is unable to be achieved
Solution Approach 1:
The system performs preliminary simulations and AI analysis to determine the optimal multi-planar shape before actual manufacturing begins. By predicting the best shape configuration in advance using machine learning models trained on historical manufacturing data, the system avoids time-consuming trial-and-error adjustments during production, thereby improving manufacturing efficiency and reducing time loss.
Solution Approach 2:
The system creates virtual copies or digital twins of the semiconductor chip designs and tests multiple shape configurations in simulation environments. By copying the manufacturing process into a virtual space for testing and optimization, the system can evaluate different multi-planar shapes without affecting actual production, enabling efficient shape prediction before physical manufacturing.
2Manufacturing precision
If multiple shape simulations are run to find optimal 3D multi-planar shape, then surface area yield is optimized, but computational complexity and processing requirements increase
Solution Approach 1:
The shape optimization process is divided into multiple independent simulation stages, each evaluating specific geometric parameters or design aspects. The AI system processes shape simulations in segments rather than as a monolithic complex task, allowing parallel computation and reducing the burden on the computational system while still achieving comprehensive surface area yield optimization.
Solution Approach 2:
The system uses temporary, disposable computational models and simulation instances that are created, executed, and discarded rapidly during the shape optimization process. Instead of maintaining complex permanent computational structures, the system generates lightweight simulation copies that consume minimal resources, enabling multiple shape evaluations without excessive computational complexity.
3Area of stationary object
If AI and machine learning are applied to predict optimal chip shapes, then area utilization is improved, but system implementation complexity increases
Solution Approach 1:
The AI and machine learning system is designed as a universal platform that can handle multiple chip design scenarios, process technologies, and shape optimization tasks through a single integrated system. By creating a multi-functional AI framework that adapts to different manufacturing requirements rather than requiring separate specialized systems for each case, the system improves area utilization across various applications while controlling overall implementation complexity through code reusability and standardized interfaces.
Data Source
AI summary
Predictive multi-planar semiconductor manufacturing systems and methods are provided including a processor, an artificial intelligence unit in communication with the processor, and a computer readable memory with processing instructions in communication with the processor. The manufacturing system receives and analyzes semiconductor design and manufacturing process rules and data and dimensions for a user's desired semiconductor. The artificial intelligence unit is configured to run simulations trying multiple three-dimensional, multi-planar shapes and analyzing for highest surface area yield based on the design and manufacturing process rules and data and the dimensions. The artificial intelligence unit is further configured to determine a three-dimensional, multi-planar shape for the desired semiconductor to optimize surface area based on the simulations and to construct the three-dimensional, multi-planar shape that optimizes surface area.