Ryoichi Ishihara

Associate Professor, Group leader

Qutech, Dep. Quantum and Computer Engineering, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology

Ishihara-lab focuses on the integration technologies for unconventional electronic systems; quantum computers, quantum sensors, neuromorphic computers, and biodegradable sensors. Our work involves new materials, scalable fabrication of electronic and photonic devices, and 3D heterogeneous integration, aiming to realize unconventional electronic systems.

Towards high yield spin qubit generation in diamond: generative deep neural network modeling and characterization of Tin implantation damage in diamond

Diamond color centers present promising prospects for creating an effective solid-state interface between spin and photon. This interface is key for developing a scalable and modular quantum computer using the spins of these color centers. To achieve practical on-chip quantum computing, networking, and sensing systems, it’s essential to have a substantial amount of spin qubits that are spatially precise, functionally reliable, and of high quality. This means they should have long coherence times, and lifetime-limited, consistent optical properties. The prominent method for creating these color centers in a controlled manner is ion implantation. This process involves embedding defect ions into the substrate, followed by a thermal annealing process to activate these defect centers, transforming them into spin qubits.

However, this ion implantation technique has a yield of < 5% for generating effective spin qubits. This low yield mostly stems from substrate amorphization, complex material damage, crystal defect formation, etc. in the diamond because of high energy ion bombardment. These ion implant-related defects/damages also significantly degrade the coherence and optical properties of the generated spin qubits, which is not suitable for any practical system with a large number of qubits. To improve the yield and qubit quality, we need to repair the ion implant damages efficiently. For any efficient damage repair, we need to carefully characterize and identify the implant-related defects in the diamond. In this project, we use a generative deep neural network modeling for modeling tin (Sn) ion implantation-related damage in diamond substrates.

Project goal/tasks:

• Measure/collect information on Sn ion implantation damages in diamonds using various material characterization tools in the cleanroom
• Use machine learning models to identify anomalies or unexpected patterns in the substrate post-implantation
• Predict the extent and type of damage based on various implantation parameters
• Prepare/provide guidelines for an effective damage repair process recipe.

References:

[1]. Ishihara, R. et al. 3D Integration Technology for Quantum Computer based on Diamond Spin Qubits. 2021 Ieee Int Electron Devices Meet Iedm 00, 14.5.1-14.5.4 (2021).

[2]. Kim, B. et al. Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation. J. Phys. D: Appl. Phys. 56, 384005 (2023).

[3]. Klein, K. M., Park, C. & Tasch, A. F. Modeling of cumulative damage effects on ion-implantation profiles. Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. At. 59, 60–64 (1991).

Interested? Please contact Ryoichi Ishihara r.ishihara@tudelft.nl or Salahuddin Nur <S.Nur@tudelft.nl>