3D Semiconductor SER Prediction via Particle Strike Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting soft error rates (SER) in 3D semiconductor devices are inaccurate, leading to potential data loss due to particle strikes from cosmic rays and radioactive emissions, particularly in three-dimensional semiconductor devices.
Innovation Solution
A simulation method and system that generates a simulation environment based on layout and netlist information, performs particle strike simulations, calculates collected charge quantities, and modifies the semiconductor circuit design to reduce SER, using techniques like Monte-Carlo simulations and SPICE modeling to enhance accuracy and resistance to soft errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SER prediction techniques are used for 3D semiconductor devices, then the prediction process is simple, but the accuracy of SER prediction is insufficient
Solution Approach 1:
The simulation system is divided into multiple independent modules: a particle transport module for simulating particle trajectories, a charge deposition module for calculating charge distribution, and a circuit response module for analyzing soft error rates. This segmentation allows each module to be optimized independently while maintaining overall system accuracy for 3D semiconductor device SER prediction
Solution Approach 2:
A dedicated simulation system acts as an intermediary between the complex 3D device structure and the SER prediction requirement. The system uses detailed device geometry models and particle transport simulations as intermediate steps to bridge the gap between physical device characteristics and soft error rate metrics, achieving high accuracy without requiring direct complex analysis
2Measurement precision
If detailed particle strike simulation is performed to improve SER prediction accuracy, then measurement precision improves, but computational time and resources increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing particle transport parameters, charge collection efficiencies, and device sensitivity maps before actual SER prediction. These pre-computed data structures enable rapid querying and reduce the computational burden during actual simulation runs, maintaining high accuracy while reducing computation time
Solution Approach 2:
The simulation system dynamically adjusts parameter precision based on the prediction stage and device characteristics. For example, using simplified particle transport models for initial screening and more detailed models for final accuracy verification, or adapting mesh resolution and particle sampling rates based on device sensitivity analysis, thereby optimizing the balance between accuracy and computational efficiency
Data Source
AI summary
A soft error rate (SER) associated with a design of a semiconductor circuit may be predicted based on implementing a simulation associated with the design. The simulation may include generating a simulation environment based on information indicating the design, performing a particle strike simulation based on the simulation environment to generate charge deposition information, and calculating a collected charge quantity from the charge deposition information. A determination may be made whether the SER predicted based on the collected charge quantity at least meets a threshold. The design may be modified, and the simulation repeated, if the predicted SER value meets a threshold value. A semiconductor circuit may be manufactured based on the design if the predicted SER value is less than the threshold value.