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Efficient Memory Circuits Yield Analysis and Optimization Framework via Meta-Learning
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DOI：10.1109/TCSII.2024.3376388.png)
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Abstract
En 中文
Yield optimization is a computationally intensive process that requires repeated yield estimation. In this brief, we propose a yield-driven optimization process based on meta-model yield analysis for memory circuits. Choosing the Artificial Neural Network (ANN) as surrogate model, we propose a model generation strategy to learn better initialization of network parameters via meta-learning. We improve the Differential Evolution (DE) algorithm to guide the optimization process and fine-tune the yield surrogate model in each iteration with limited simulation overheads. The experimental results validated on the memory circuits show that the proposed method can achieve final results with higher yields and performs high accuracy and robustness compared to state-of-the-art methods.
Keywords:
Integrated circuit modeling
Optimization
Task analysis
Training
Analytical models
Yield estimation
Metalearning
Yield optimization
meta-learning
baseline model
memory circuits
Journal
I
IF:
4.9
Papers:
8.8K
Citations:
2.5W
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