MS4.05 High-resolution electron microscopy for the design and scalability of neuromorphic quantum photonics S. Taheriniya1, R. Xu1, A. Varri2, R. Bankwitz1, S. Jo2, R. R. Schröder3, W. Pernice1,2 1Heidelberg University, Kirchhoff-Institute for Physics, Heidelberg, Germany 2University of Münster, Institute of Materials Physics, Münster, Germany 3Heidelberg University, BioQuant, Heidelberg, Germany their This is intended properties. Even minor deviations Future technology is being defined by breakthroughs around the concept of neuromorphic quantum photonics, where high-speed, low-power, and highly scalable platforms are set to revolutionize computing, communication, and information processing1–3. At the heart of these advancements lies the precise design and fabrication of integrated photonic structures, which must be engineered with nanometer-scale accuracy to ensure optimal performance4–6. To seamlessly transition from theoretical designs to real-world implementation, it is essential to ensure that fabricated devices precisely match in design can significantly impact light-matter interactions, which are critical for the performance and reliability of quantum applications. particularly Scanning/Transmission Electron Microscopy (S/TEM), play a transformative role by providing sub- nanometer insights into material properties, interface quality, and fabrication-induced imperfections. Incorporating S/TEM-based analysis into loop that automates manufacturing, enables post-fabrication tuning, and optimizes material processing, ensuring scalable, stable, and high-performance photonic platforms for quantum applications. This work presents a systematic approach to photonic device analysis, emphasizing optimized sample preparation using Focused imaging, spectroscopy (EDS, EELS), phase mapping, and microstructural analysis4,6,7. Applied across a diverse range of integrated photonic platforms, this methodology enhances fabrication precision, enabling highly tunable device properties. Additionally, it refines simulation parameters, paving the way for scalable solutions and models tailored for real-world applications. fabrication establishes a feedback where high-resolution Ion Beam (FIB) milling with minimized artifacts for high-resolution characterization techniques, (1) Ashtiani, F.; Geers, A. J.; Aflatouni, F. An On-Chip Photonic Deep Neural Network for Image Classification. Nature 2022, 606 (7914), 501–506. (2) Mehonic, A.; Kenyon, A. J. Brain-Inspired Computing Needs a Master Plan. Nature 2022, 604 (7905), 255–260. (3) Bai, Y.; Xu, X.; Tan, M.; Sun, Y.; Li, Y.; Wu, J.; Morandotti, R.; Mitchell, A.; Xu, K.; Moss, D. J. Photonic Multiplexing Techniques for Neuromorphic Computing. Nanophotonics 2023, 12 (5), 795– 817. (4) Varri, A.; Taheriniya, S.; Brückerhoff-Plückelmann, F.; Bente, I.; Farmakidis, N.; Bernhardt, D.; Rösner, H.; Kruth, M.; Nadzeyka, A.; Richter, T.; Wright, C. D.; Bhaskaran, H.; Wilde, G.; Pernice, W. H. P. Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation. Adv. Mater. 2024, 36 (8), 2310596. (5) Brückerhoff-Plückelmann, F.; Feldmann, J.; Gehring, H.; Zhou, W.; Wright, C. D.; Bhaskaran, H.; Pernice, W. Broadband Photonic Tensor Core with Integrated Ultra-Low Crosstalk Wavelength Multiplexers. Nanophotonics 2022, 11 (17), 4063–4072. (6) Xu, R.; Taheriniya, S.; Varri, A.; Ulanov, M.; Konyshev, I.; Krämer, L.; McRae, L.; Ebert, F. L.; Bankwitz, J. R.; Ma, X.; Ferrari, S.; Bhaskaran, H.; Pernice, W. H. P. Mode Conversion Trimming in Asymmetric Directional Couplers Enabled by Silicon Ion Implantation. Nano Lett. 2024, 24 (35), 10813–10819. 241