HYBRAIN Scientific papers
These publications have been created with financial support from the European Union’s Horizon Europe research and innovation programme HYBRAIN project under grant agreement No 101046878.
Dong, B., Brückerhoff-Plückelmann, F., Meyer, L. et al., "Partial coherence enhances parallelized photonic computing" Nature 632, 55–62 (2024). https://doi.org/10.1038/s41586-024-07590-y
Ovvyan, Anna P et al., "An electroluminescent and tunable cavity-enhanced carbon-nanotube-emitter in the telecom band" Nature communications vol. 14,1 3933. 4 Jul. 2023, doi:10.1038/s41467-023-39622-y
S. Aggarwal et al., "Design for Robust and Efficient Neuromorphic Photonic Accelerator" 2022 IEEE International Conference on Emerging Electronics (ICEE), Bangalore, India, 2022, pp. 1-4, doi: 10.1109/ICEE56203.2022.10117902
Youngblood, N., Ríos Ocampo, C.A., Pernice, W.H.P. et al. "Integrated optical memristors" Nat. Photon. 17, 561–572 (2023).
https://doi.org/10.1038/s41566-023-01217-w
Bruno Lopez-Rodriguez, Roald van der Kolk, Samarth Aggarwal, Naresh Sharma, Zizheng Li, Daniel van der Plaats, Thomas Scholte, Jin Chang, Simon Gröblacher, Silvania F. Pereira, Harish Bhaskaran, and Iman Esmaeil Zadeh "High-Quality Amorphous Silicon Carbide for Hybrid Photonic Integration Deposited at a Low Temperature" ACS Photonics 2023 10 (10), 3748-3754.
https://doi.org/10.1021/acsphotonics.3c00968
Aggarwal, Samarth, Farmakidis, Nikolaos, Dong, Bowei, Lee, June Sang, Wang, Mengyun, Xu, Zhiyun and Bhaskaran, Harish. "All optical tunable RF filter using elemental antimony" Nanophotonics, vol. 13, no. 12, 2024, pp. 2223-2229.
https://doi.org/10.1515/nanoph-2023-0654
B. Lu, Y. Xia, Y. Ren, M. Xie, L. Zhou, G. Vinai, S. A. Morton, A. T. S. Wee, W. G. van der Wiel, W. Zhang, P. K. J. Wong. "When Machine Learning Meets 2D Materials: A Review" Adv. Sci. 2024, 2305277. https://doi.org/10.1002/advs.202305277
A. Varri, S. Taheriniya, F. Brückerhoff-Plückelmann, I. Bente, N. Farmakidis, D. Bernhardt, H. Rösner, M. Kruth, A. Nadzeyka, T. Richter, C. D. Wright, H. Bhaskaran, G. Wilde, W. H. P. Pernice "Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation" Adv. Mater. 2023, 2310596. https://doi.org/10.1002/adma.202310596
Alegre-Ibarra et al. "brains-py, A framework to support research on energy-efficient unconventional hardware for machine learning" Journal of Open Source Software, 8(90), 5573 (2023). https://doi.org/10.21105/joss.05573
Dong, B., Aggarwal, S., Zhou, W. et al. "Higher-dimensional processing using a photonic tensor core with continuous-time data" Nat. Photon. 17, 1080–1088 (2023). https://doi.org/10.1038/s41566-023-01313-x
Manuel Le Gallo, Corey Lammie, Julian Büchel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch. "Using the IBM analog in-memory hardware acceleration kit for neural network training and inference" APL Mach. Learn. 1 December 2023; 1 (4): 041102. https://doi.org/10.1063/5.0168089
Jaeger, H., Noheda, B. & van der Wiel, W.G. "Toward a formal theory for computing machines made out of whatever physics offers" Nat Commun 14, 4911 (2023). https://doi.org/10.1038/s41467-023-40533-1
Brückerhoff-Plückelmann, Frank, Bente, Ivonne, Wendland, Daniel, Feldmann, Johannes, Wright, C. David, Bhaskaran, Harish and Pernice, Wolfram. "A large scale photonic matrix processor enabled by charge accumulation" Nanophotonics, vol. 12, no. 5, 2023, pp. 819-825. https://doi.org/10.1515/nanoph-2022-0441
Sarwat, S. G., Zhou, Y., Warner, J., & Bhaskaran, H. (2023). Atomically thin optomemristive feedback neurons." Springer Nature, Nature Nanotechnology, 2023, Issn: 1748-3387. https://doi.org/10.1038/s41565-023-01391-6
Dominique J. Kösters, Bryan A. Kortman, Irem Boybat, Elena Ferro, Sagar Dolas, Roberto Ruiz de Austri, Johan Kwisthout, Hans Hilgenkamp, Theo Rasing, Heike Riel, Abu Sebastian, Sascha Caron, Johan H. Mentink; Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics. APL Machine Learning 1 March 2023; 1 (1): 016101. https://doi.org/10.1063/5.0116699.
Van de Ven B., Alegre-Ibarra U., Lemieszczuk P. J., Bobbert P. A., Ruiz Euler H.-C., van der Wiel W. G. , "Dopant network processing units as tuneable extreme learning machines" Frontiers in Nanotechnology volume 5, 2023, Issn: 2673-3013, doi: 10.3389/fnano.2023.1055527.
J. Büchel et al., "Gradient descent-based programming of analog in-memory computing cores" 2022 International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2022, pp. 33.1.1-33.1.4, doi: 10.1109/IEDM45625.2022.10019486.
Farmakidis, N., Yu, H., Lee, J. S., Feldmann, J., Wang, M., He, Y., Aggarwal, S., Dong, B., Pernice, W. H. P., & Bhaskaran, H., "Scalable High-Precision Trimming of Photonic Resonances by Polymer Exposure to Energetic Beams" Nano letters, 23(11), 4800–4806. https://doi.org/10.1021/acs.nanolett.3c00220
Zolfagharinejad, M., Alegre-Ibarra, U., Chen, T. et al., "Brain-inspired computing systems: a systematic literature review" Eur. Phys. J. B 97, 70 (2024). https://doi.org/10.1140/epjb/s10051-024-00703-6.
Farmakidis, N., Dong, B. & Bhaskaran, H., "Integrated photonic neuromorphic computing: opportunities and challenges" Nat Rev Electr Eng 1, 358–373 (2024). https://doi.org/10.1038/s44287-024-00050-9.
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 letters, 10.1021/acs.nanolett.4c02065. Advance online publication. https://doi.org/10.1021/acs.nanolett.4c02065