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"Engineers at Australia’s national science agency, CSIRO, have performed a world-first use of quantum machine learning to fabricate semiconductors. The research could reshape the way future microchips are designed. ...
The team was particularly interested in modelling the Ohmic contact resistance of the semiconductor material. This property is a measure of the electrical resistance where the semiconductor comes into contact with a metal and the current flows easily between the materials in both directions.
Modelling Ohmic contact resistance is critical to semiconductor design and fabrication, but it’s also a property which is notoriously difficult to model. ...
The team developed an innovative Quantum Kernel-Aligned Regressor (QKAR) architecture.
Their QKAR setup included a Pauli-Z quantum feature map – a mathematical operator which can translate classical data into quantum states in the form of 5 quantum bits, or qubits.
Once data is mapped to the qubits, a quantum kernel alignment layer is used to perform the machine learning. ..."
From the abstract:
"Modeling complex semiconductor fabrication processes such as Ohmic contact formation remains challenging due to high-dimensional parameter spaces and limited experimental data.
While classical machine learning (CML) approaches have been successful in many domains, their performance degrades in small-sample, nonlinear scenarios.
In this work, quantum machine learning (QML) is investigated as an alternative, exploiting quantum kernels to capture intricate correlations from compact datasets. Using only 159 experimental GaN HEMT samples, a quantum kernel-aligned regressor (QKAR) is developed combining a shallow Pauli-Z feature map with a trainable quantum kernel alignment (QKA) layer.
All models, including seven baseline CML regressors, are evaluated under a unified PCA-based preprocessing pipeline to ensure a fair comparison. QKAR consistently outperforms classical baselines across multiple metrics (MAE, MSE, RMSE), achieving a mean absolute error of 0.338 Ω·mm when validated on experimental data.
Noise robustness and generalization are further assessed through cross-validation and new device fabrication.
These findings suggest that carefully constructed QML models can provide predictive advantages in data-constrained semiconductor modeling, offering a foundation for practical deployment on near-term quantum hardware. While challenges remain for both QML and CML, this study demonstrates QML's potential as a complementary approach in complex process modeling tasks."
Fig. 7 The process of how to build the QML model.
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