Semiconductor Picking Sequence for Matched Substrate Assembly
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Solution Overview
Problem
Existing semiconductor pick-and-place devices lack an optimized picking sequence that ensures semiconductors mounted on a substrate operate optimally and efficiently, leading to potential performance issues and increased costs due to unnecessary semiconductor usage and time-consuming classification.
Innovation Solution
A method and control device for determining a picking sequence for a semiconductor pick-and-place device that considers performance parameters of individual semiconductors, using sensors and machine learning algorithms to optimize substrate loading, minimizing travel times and semiconductor variations, thereby enhancing module performance and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If semiconductors are mounted on substrate without optimized picking sequence, then assembly process is simple, but module performance is suboptimal and semiconductor usage is inefficient
Solution Approach 1:
The system determines the optimized picking sequence before the actual assembly process by evaluating performance parameters of all available semiconductors. This preliminary determination ensures that semiconductors are selected and mounted in an optimal sequence, maximizing module performance while minimizing the need for reclassification or remounting during production.
Solution Approach 2:
The system reads performance parameters of semiconductors from storage locations, evaluates them against target values, and uses this feedback information to dynamically determine the optimal picking sequence. This feedback mechanism allows the system to adapt the mounting sequence based on actual semiconductor characteristics, ensuring optimal performance while reducing waste.
2Reliability
If traditional picking sequence is used, then production time is reduced, but semiconductor variations cause performance issues
Solution Approach 1:
The system changes the picking sequence parameters based on the performance parameters of available semiconductors. By evaluating characteristics such as threshold voltage, transconductance, and breakdown voltage, the system dynamically adjusts the mounting sequence to minimize performance variations across parallel-connected semiconductors, ensuring consistent module performance without excessive production time loss.
3Reliability
If semiconductors are classified during production, then performance matching is improved, but production time increases significantly
Solution Approach 1:
The system performs semiconductor evaluation and sequence determination before the assembly process begins. By reading performance parameters from storage and determining the optimal picking sequence in advance, the system eliminates the need for time-consuming classification operations during production, thereby maintaining high productivity while ensuring proper semiconductor matching.
4Reliability
If more semiconductors are used to ensure performance, then reliability is improved, but cost and complexity increase
Solution Approach 1:
The system uses performance parameter evaluation to optimize the picking sequence, ensuring that the minimum necessary number of semiconductors are mounted to achieve the desired performance. By selecting semiconductors with characteristics closest to target values and arranging them in an optimal sequence, the system reduces the quantity of semiconductors needed while maintaining or improving reliability, thereby reducing cost and complexity.
Data Source
AI summary
The disclosure relates to a method for determining a picking sequence for a semiconductor pick-and-place device which is designed to remove semiconductors from a store of semiconductors according to the picking sequence and to assemble them on a substrate, wherein the method comprises the following steps: for each semiconductor from the store of semiconductors, providing a value for at least one performance parameter; and determining the picking sequence on the basis of the provided values for the at least one performance parameter in such a way that a substrate can be populated optimally in respect of the at least one performance parameter on the basis of the picking sequence.