Quantum computing is advancing rapidly from isolated research devices toward increasingly complex and scalable systems. This growth is driven by cloud deployments and breakthroughs in artificial intelligence, optimization, and simulations, pushing the market to a 41.8% CAGR from 2025 to 2030^{[Source: https://www.marketsandmarkets.com]}. But scaling a quantum processor is not only a challenge for the qubits, couplers or Josephson junctions themselves; it is also a challenge for the electronics that control, bias, and read out those devices sitting on a cryo-electronics wafer integrated alongside the qubit wafer. Much of the supporting electronics must operate at cryogenic temperatures, often only a few degrees above absolute zero. As more control and readout functionality moves closer to the quantum processor, the need for accurate models of semiconductor devices operating at these temperatures becomes increasingly important.

A common challenge modeling engineers face is to develop accurate and computationally efficient device models when low temperature physics is either not fully understood, or available compact models are not yet standardized which is further exacerbated by lack of specialized measurement setup needed at cryogenic temperatures. Parameters such as threshold voltage, carrier transport, leakage, and transconductance also shift substantially. Additional low-temperature effects also become important, including dopant freeze-out and changes in carrier statistics and band structure below 10K.

This creates a new challenge for device modeling and circuit design engineers:

How do you develop accurate, simulation-ready device models at cryogenic temperatures without spending months manually tuning parameters or developing new physics-based equations from scratch?

This matters because without accurate models, engineers cannot confidently simulate and optimize cryogenic control circuits, readout electronics, low-noise amplifiers, and other critical components of a quantum system.

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