Follow the steps below to see a simplified workflow of TRUST AI. Input material properties and observe the simulated process of FEM analysis, AI training, and result visualization.
Welcome to the TRUST AI Demo
This interactive demonstration will guide you through a simplified workflow of our AI-powered reliability prediction tool for High Bandwidth Memory (HBM).
You will define material properties, simulate a Finite Element Analysis (FEM), observe the AI dataset curation and training process, and finally, see a conceptual visualization of the AI's prediction.
Click the button below to begin.
Step 1: Define Material Properties
Step 2: Run FEM Simulation
Based on the defined material properties, the system would now perform a detailed Finite Element Method (FEM) simulation to analyze thermal stress and strain distributions within the HBM structure.
Step 3: Curate AI Training Dataset
The results from numerous FEM simulations (under various conditions and parameters) are collected, processed, and structured to form a comprehensive dataset for training our AI model.
Step 4: Train AI Model
Our sophisticated AI model (e.g., leveraging U-Net architecture) is trained on the curated dataset to learn the complex relationships between HBM design parameters, material properties, and thermal fatigue reliability.
Step 5: Validate and Visualize AI Prediction
The trained AI model provides rapid and accurate predictions for new HBM designs. Here's a conceptual visualization of a reliability prediction:
