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Using a fully automated cryogenic wafer prober, we measured superconductor fabrication process control monitors and simple integrated circuits on 200 mm wafers at 4.4 K, including SQIF-based magnetic field sensors, SQUID-based circuits for measuring inductors, Nb/Al-AlOx/Nb Josephson junctions, test structures for measuring critical current of superconducting wires and vias, resistors, etc., to demonstrate the feasibility of using the system for characterizing niobium superconducting devices and integrated circuits on a wafer scale. Data on the wafer-scale distributions of the residual magnetic field, junction tunnel resistance, energy gap, inductance of multiple Nb layers, critical currents of interlayer vias are presented. Comparison with existing models is made. The wafers were fabricated in the SFQ5ee process, the fully planarized process with eight niobium layers and a layer of kinetic inductors, developed for superconductor electronics at MIT Lincoln Laboratory. The cryogenic wafer prober was developed at HPD/ FormFactor, Inc.

Spins in solid-state materials, molecules, and other chemical systems have the potential to impact the fields of quantum sensing, communication, simulation, and computing. In particular, color centers in diamond, such as negatively charged nitrogen vacancy (NV\\(^-\\)) and silicon vacancy centers (SiV\\(^-\\)), are emerging as quantum platforms poised for transition to commercial devices. A key enabler stems from the semiconductor-like platform that can be tailored at the time of growth. The large growth parameter space makes it challenging to use intuition to optimize growth conditions for quantum performance. In this paper, we use supervised machine learning to train regression models using different synthesis parameters in over 100 quantum diamond samples. We train models to optimize NV\\(^-\\) defects in diamond for high sensitivity magnetometry. Importantly, we utilize a magnetic-field sensitivity figure of merit (FOM) for NV magnetometry and use Bayesian optimization to identify critical growth parameters that lead to a 300% improvement over an average sample and a 55% improvement over the previous champion sample. Furthermore, using Shapley importance rankings, we gain new physical insights into the most impactful growth and post-processing parameters, namely electron irradiation dose, diamond seed depth relative to the plasma, seed miscut angle, and reactor nitrogen concentration. As various quantum devices can have significantly different material requirements, advanced growth techniques such as plasma-enhanced chemical vapor deposition (PE-CVD) can provide the ability to tailor material development specifically for quantum applications.