most citedAtOMICS: A neural network-based Automated Optomechanical Intelligent Coupling System for testing and characterization of silicon photonics chiplets

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.mes-hall2026

Rapid Charge Stability Diagram Generation from Device-level Modeling of Semiconductor Quantum Dots

Ron Nodel, David W. Kanaar, Connor Nasseraddin +5

Self-consistent Schrödinger-Poisson calculations are a powerful tool for predicting the behavior of layered semiconductor quantum dot devices. However, characterization of charge s…

cond-mat.mes-hall2026

Spectroscopy of low-lying valley states in hot Si/SiGe quantum dots

Connor Nasseraddin, Heun Mo Yoo, Tanner M. Janda +1

The presence of low-lying valley states in Si may hinder the development of large-scale spin-based quantum processors. Rapid prototyping of novel Si/SiGe heterostructures and gate…

cond-mat.mes-hall2026

Directly visualizing the energy level structure of quantum dot molecules

Heun Mo Yoo, Tanner M. Janda, Connor Nasseraddin +1

The orbital, spin and valley degrees of freedom in silicon quantum dots support many modes of spin qubit operation. However, it is generally challenging to obtain information about…

cond-mat.mes-hall2026

Microwave response of electrically driven spins in a three-qubit quantum processor

Tanner M. Janda, Heun Mo Yoo, Connor Nasseraddin +3

In electric dipole spin resonance (EDSR), a single spin is electrically driven in the field gradient produced by a micromagnet. While EDSR has enabled high fidelity gate operations…

eess.IV2022★ 1 cited

AtOMICS: A neural network-based Automated Optomechanical Intelligent Coupling System for testing and characterization of silicon photonics chiplets

Jaime Gonzalo Flor Flores, Connor Nasseraddin, Jim Solomon +3

Recent advances in silicon photonics promise to revolutionize modern technology by improving performance of everyday devices in multiple fields. However, as the industry moves into…