This work presents a comprehensive analytical and simulation-based study of superconducting quantum chip architectures, spanning both 2D planar and 3D-integrated designs. We first design and simulate a planar quantum chip layout using Qiskit Metal, which is imported into Ansys HFSS (High Frequency Structure Simulator) to construct a 3D model and perform electromagnetic simulations. A detailed analysis using Driven Modal, Eigenmode, Magnetostatic, and Q3D Capacitance Extraction solvers ensures our simulations accurately reflect physical behavior. The results are validated against analytical models from microwave engineering. pyEPR (Python Energy Participation Ratio) is used to extract key quantum metrics, including eigenfrequencies, quality factors, decoherence times, anharmonicity, cross-Kerr interactions, and participation ratios. Building on this planar design, we extend our study to a 3D-integrated flip-chip architecture featuring two superconducting qubits fabricated on separate high-resistivity silicon substrates. Using HFSS and analytical modeling, we show that detuned qubits maintain stable eigenfrequencies, quality factors, decoherence times, anharmonicity, and cross-Kerr interactions over varying substrate separation values. However, the introduction of dielectric loss tangents reduces quality factors which degrades coherence times and introduces an additional dephasing. These findings highlight the potential of 3D quantum integration, given minimal material imperfections.The planar and 3D designs together form a cohesive investigation into next-generation quantum hardware, with the 2D layout currently taped-out and under fabrication.
This work presents a comprehensive analytical and simulation-based study of superconducting quantum chip architectures, spanning both 2D planar and 3D-integrated designs. We first design and simulate a planar quantum chip layout using Qiskit Metal, which is imported into Ansys HFSS (High Frequency Structure Simulator) to construct a 3D model and perform electromagnetic simulations. A detailed analysis using Driven Modal, Eigenmode, Magnetostatic, and Q3D Capacitance Extraction solvers ensures our simulations accurately reflect physical behavior. The results are validated against analytical models from microwave engineering. pyEPR (Python Energy Participation Ratio) is used to extract key quantum metrics, including eigenfrequencies, quality factors, decoherence times, anharmonicity, cross-Kerr interactions, and participation ratios. Building on this planar design, we extend our study to a 3D-integrated flip-chip architecture featuring two superconducting qubits fabricated on separate high-resistivity silicon substrates. Using HFSS and analytical modeling, we show that detuned qubits maintain stable eigenfrequencies, quality factors, decoherence times, anharmonicity, and cross-Kerr interactions over varying substrate separation values. However, the introduction of dielectric loss tangents reduces quality factors which degrades coherence times and introduces an additional dephasing. These findings highlight the potential of 3D quantum integration, given minimal material imperfections.The planar and 3D designs together form a cohesive investigation into next-generation quantum hardware, with the 2D layout currently taped-out and under fabrication.
This work presents a combined analytical and simulation-based study of a 3D-integrated quantum chip architecture. We model a flip-chip-inspired structure by stacking two superconducting qubits fabricated on separate high-resistivity silicon substrates through a dielectric interlayer. Utilizing rigorous Ansys High-Frequency Structure Simulator (HFSS) simulations and analytical models from microwave engineering, we evaluate key quantum metrics such as eigenfrequencies, Q-factors, decoherence times, anharmonicity, cross-Kerr, participation ratios, and qubit coupling energy to describe the performance of the quantum device as a function of integration parameters. The integration parameters include the thickness and the quality of the dielectric interlayer. For detuned qubits, these metrics remain mostly invariant with respect to the substrate separation. However, introducing dielectric interlayer loss decreases the qubit quality factor, which consequentially degrades the relaxation time of the qubit. It is found that for the structure studied in this work, the stacked chip distance can be as small as 0.5 mm. These findings support the viability of 3D quantum integration as a scalable alternative to planar architectures, while identifying key limitations in qubit coherence preservation due to lossy interlayer materials.
Quantum computing is advancing rapidly, with superconducting quantum computing (SQC) emerging as one of the most promising platforms for scalable quantum information processing. In contrast to fabrication-focused approaches prevalent in current literature, this work presents a fully simulation-based design and tuning methodology for a small-scale superconducting quantum chip - addressing practical limitations posed by fabrication resource constraints - and consequently excludes physical chip fabrication or real-time performance evaluation (e.g., fidelity and error rate measurements). A meticulous, step-by-step electromagnetic simulation was conducted on the chip, designed using Qiskit Metal and Ansys Electronics, with qubit parameters tuned and verified using the Lumped Oscillator Model (LOM) and Energy Participation Ratio (EPR) methods. The design achieves good parameter convergence with relative tuning error rates (Δ%) below 0.05% after 15 adaptive passes and approximately 50,000 solved mesh elements, yielding an Energy Participation Ratio (EPR) of 91.8 % for the chip substrate (Silicon). Furthermore, the study includes a theoretical comparison between two fundamental qubit-qubit coupling schemes. A key contribution of this work lies in systematically analyzing how geometric variations in qubit design affect critical quantum parameters such as qubit frequencies, anharmonicities, coupling strengths, and dispersive shifts - thereby offering a comprehen-sive, simulation-driven framework for early-stage quantum chip prototyping.
This paper presents the design, simulation, and fabrication process of a superconducting single qubit utilizing Josephson junctions in both planar and flip-chip architectures. The Xmon qubit, characterized by its cross-shaped coplanar waveguide structure and high scalability, is employed to demonstrate the enhancement in coherence, connectivity, and control speed. The Xmon qubit was modeled and validated using Python, Ansys HFSS, and Sonnet software. The fabrication process, including the transmission line and Josephson junction, is also introduced.
We propose SPICE-Q, a SPICE-inspired design-technology co-optimization framework for superconducting quantum processors. Rather than replacing tools such as HFSS, Qiskit Metal, pyEPR, SQcircuit, SQuADDS, scqubits, or QuTiP, SPICE-Q aims to connect them through a unified, traceable data chain spanning process rules, layout, electromagnetic simulation, energy-participation-ratio and circuit quantization, Hamiltonian extraction, noise analysis, cryogenic test, and manufacturing feedback. The central mapping is from process and PDK constraints to layout geometry, electromagnetic modes, equivalent circuit parameters, effective Hamiltonians, and finally metrics such as frequency, coupling, anharmonicity, decoherence, readout performance, and yield. This flow must capture Josephson-junction variability, transmon frequency allocation, resonator and Purcell constraints, coupler crosstalk, microwave routing, 3D interconnects, material/interface loss, package modes, and wafer-scale process statistics. By introducing standardized model interfaces, statistical parameter models, model cards, version governance, and closed-loop calibration from cryogenic and fabrication data, SPICE-Q frames superconducting quantum-chip design as an engineering workflow rather than a collection of isolated simulations. We argue that scalable and fault-tolerant quantum processors will require such a continuous model chain from device physics and electromagnetic fields to quantum dynamics, noise, manufacturability, and system-level yield.
In superconducting quantum processors, the predictability of device parameters is of increasing importance as many labs scale up their systems to larger sizes in a 3D-integrated architecture. In particular, the properties of superconducting resonators must be controlled well to ensure high-fidelity multiplexed readout of qubits. Here we present a method, based on conformal mapping techniques, to predict a resonator's parameters directly from its 2D cross-section, without computationally heavy and time-consuming 3D simulation. We demonstrate the method's validity by comparing the calculated resonator frequency and coupling quality factor with those obtained through 3D finite-element-method simulation and by measurement of 15 resonators in a flip-chip-integrated architecture. We achieve a discrepancy of less than 2% between designed and measured frequencies, for 6-GHz resonators. We also propose a design method that reduces the sensitivity of the resonant frequency to variations in the inter-chip spacing.
Translating superconducting quantum chip layouts into physically meaningful simulation records is a critical step in quantum hardware development, yet current datasets rarely capture the electromagnetic (EM) effects involved in this process. We propose SQChip-EM, an open layout to EM simulation dataset based on Qiskit Metal that links parameterized layout designs to Ansys HFSS and Q3D simulation results. Unlike abstract circuit datasets, SQChip-EM preserves layout dependent EM evidence through GDSII files, raw simulation outputs, and JSON annotations covering frequencies, anharmonicity, dispersive shifts, and coupling parameters. These annotations form reusable simulation level ''virtual chip'' records for downstream workflows such as initialization, calibration tuning, and control simulation. SQChip-EM also includes three illustrative benchmark tasks, including design to metric regression, specification feasibility classification, and inverse design retrieval, together with one downstream workflow use case. By releasing data, code, and evaluation protocols, SQChip-EM provides a standardized data resource for ML assisted quantum electronic design automation.