AI-Driven Device Modeling For Next Generation Quantum Applications

What happened

Extracting advanced compact models and overcoming their shortcomings using a hybrid ANN approach at cryogenic temperatures. The post AI-Driven Device Modeling For Next Generation Quantum Applications appeared first on Semiconductor Engineering .

Key takeaways: Accelerate quantum system design by rapidly developing accurate device models for circuit-level simulation. Reduce manual model-tuning effort with Keysight ML Optimizer, enabling efficient, derivative-free extraction of advanced compact model parameters.

Extend compact models to cryogenic temperatures using deep learning neural networks (NN) to learn unmodeled residual behavior from 4 K or below measurement data. Quantum computing is advancing rapidly from isolated research devices toward increasingly complex and scalable systems.