Representative Via Selection for Electrodeposition Simulation
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Solution Overview
Problem
Current electrodeposition methods for three-dimensional integrated circuits (3D-ICs) face challenges in accurately simulating the electrodeposition process for vias, particularly due to the lack of consideration for via geometry and the high computational time required for detailed simulations, which limits the optimization of process parameters and increases costs.
Innovation Solution
A method utilizing an artificial neural network (ANN) to select a representative via and reduce the number of sampling points, allowing for faster simulation of the electrodeposition process while maintaining accuracy by optimizing process parameters based on filling goodness metrics, such as void length and effective seam length, to determine the optimal process window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed simulation of electrodeposition process for all vias is performed, then accuracy of process optimization is improved, but computational time increases significantly
Solution Approach 1:
The patent segments the via population into representative categories based on geometry and position. Instead of simulating all vias individually, it selects a limited number of representative vias from each category, dramatically reducing computational time while maintaining optimization accuracy through focused simulation on critical cases.
Solution Approach 2:
The patent creates simplified computational models (copies) of the electrodeposition process for representative vias. These models replicate the essential physics and chemistry of the full process but with reduced complexity, enabling faster simulation that still provides accurate guidance for process optimization across all vias.
2Manufacturing precision
If comprehensive consideration of via geometry and process parameters is included in simulation, then optimization accuracy is improved, but device complexity of the simulation model increases
Solution Approach 1:
The patent applies local quality by tailoring the simulation model complexity to the specific needs of each via category. Different representative vias use appropriately detailed models based on their geometric characteristics and process challenges, rather than uniformly applying maximum complexity to all cases. This reduces overall model complexity while maintaining necessary accuracy for each specific via type.
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 simulation time while maintaining high accuracy, enabling the optimization of electrodeposition processes for 3D-ICs by focusing on critical via geometry and process parameters, thereby improving the quality and efficiency of via filling in 3D-IC fabrication.
Implementation Method 1
A method utilizing an artificial neural network (ANN) to select a representative via and reduce the number of sampling points
Implementation Method 2
electrodeposition—the depositing of metal into vias through electroplating process
Implementation Method 3
the depositing of metal into vias through electroplating process
Data Source
AI summary
The presently claimed invention provides a method for optimizing an electrodeposition process of a plurality of vias in a wafer. Instead of simulating a large number of via on the wafer for via filling, a representative via is selected with the maximum value of critical factor, which is a function of process parameters. The filling of the representative via is simulated with different sampling points to find out the filling goodness in order to find out the optimized process windows of process parameters. An optimizer is also disclosed, which either provides sampling points or reduces sampling points under artificial neural network method. Calculation of filling goodness is used for evaluating via filling quality and further comparing among via fillings simulated at different sampling points. Consequently, the method of present invention is able to shorten the simulation time for via filling as well as provide a process window with high accuracy.