Human machine competition in intelligent laser manufacturing in semiconductor processes

Optical proximity correction (OPC) is a critical step in semiconductor manufacturing due to its high complexity and significant influence on the subsequent process steps. Conventional OPC using the Maxwell equation can become more and more challenging as a fully vectorized three-dimensional simulation is required for advanced technology nodes. Machine learning (ML) has been a promising alternative recently. This work proposes machine-learning-assisted human decision, which can be more in line with the clean room engineer's practice and can potentially surpass pure human decision and the pure machine learning approach. Using 10-step optimization in the photolithographic mask, the averaged mean square error (MSE) at the optimized cases are 6360 and 2101 for two-bar patterns and 7132 and 5931 for tri-line attackers when comparing pure human decision and ML-assisted human. The average MSEs at the first 3 steps are 26019 and 6023 for the two-bar pattern and 79979 and 7738 for the tri-line attacker when comparing pure ML and ML-assisted human. It is suggested that the strength of the ML-assisted human decision lies in the early-stage superiority over pure ML, flexibility, incorporating past experience, and a human sense that cannot be formulated concretely by statistical models.

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“…Previously, we have used real human experiments [32]. In this work, human decision is based on rules.…”

Section: B Algorithmmentioning

confidence: 99%

Optical Proximity Correction Using Machine Learning Assisted Human Decision

et al. 2023

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Optimization of laser annealing parameters based on bayesian reinforcement learning

Chang,

Feng,

Rawat

et al. 2024

J Intell Manuf

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Developing new semiconductor processes consumes tremendous time and cost. Therefore, we applied Bayesian reinforcement learning (BRL) with the assistance of technology computer-aided design (TCAD). The fixed or variable prior BRL is tested where the TCAD prior is fixed or is changed by the experimental sampling and decays during the entire RL procedure. The sheet resistance (Rs) of the samples treated by laser annealing is the optimization target. In both cases, the experimentally sampled data points are added to the training dataset to enhance the RL agent. The model-based experimental agent and a model-free TCAD Q-Table are used in this study. The results of BRL proved that it can achieve lower Rs minimum values and variances at different hyperparameter settings. Besides, two action types, i.e., point to state and increment of levels, are proven to have similar results, which implies the method used in this study is insensitive to the different action types.

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Human machine competition in intelligent laser manufacturing in semiconductor processes

Cited by 2 publications

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Optical Proximity Correction Using Machine Learning Assisted Human Decision

Optical Proximity Correction Using Machine Learning Assisted Human Decision

Optimization of laser annealing parameters based on bayesian reinforcement learning

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