in: arXiv (2025)
Brunner, Daniel; Shastri, Bhavin J.; Al-Qadasi, Mohammed A.; Barbay, Sylvain; Biasi, Stefano; Bienstman, Peter; Bilodeau, Simon; Bogaerts, Simon; Böhm, Fabian; Brennan, G.; Buckley, Sonia; Cai, Xinlun; Calvanese Strinati, Marcello; Canakci, B.; Charbonnier, Benoit; Chemnitz, Mario; Chen, Yitong; Cheung, Stanley; Chiles, Jeff; Choi, Suyeon; Christodoulides, Demetrios N.; Chrostowski, Lukas; Chu, J.; Clegg, J. H.; Cletheroe, D.; Conti, Claudio; Dai, Qionghai; Di Lauro, Luigi; Diamantopoulos, Nikolaos-Panteleimon; Dinc, Niyazi Ulas; Ewaniuk, Jacob; Fan, Shanhui; Fang, Lu; Franchi, Riccardo; Freire, Pedro; Gentilini, Silvia; Gigan, Sylvain; Giorgi, Gian Luca; Gkantsidis, C.; Gladrow, J.; Goi, Elena; Goldmann, M.; Grabulosa, A.; Gu, Min; Guo, Xianxin; Hejda, Matej; Horst, F.; Hsieh, Jih-Liang; Hu, Jianqi; Hu, Juejun; Huang, Chaoran; Hurtado, Antonio; Jaurigue, Lina; Kalinin, K. P.; Kamalian-Kopae, Morteza; Kelly, D. J.; Khajavikhan, Mercedeh; Kremer, H.; Laydevant, Jeremie; Lederman, Joshua C.; Lee, Jongheon; Lenstra, Daan; Li, Gordon H. Y.; Li, Mo; Li, Yuhang; Lin, Xing; Lin, Zhongjin; Lis, Mieszko; Lüdge, Kathy; Lugnan, Alessio; Lupo, Alessandro; Lvovsky, A. I.; Manuylovich, Egor; Marandi, Alireza; Marchesin, Federico; Massar, Serge; McCaughan, Adam N.; McMahon, Peter L.; Moralis-Pegios, Miltiadis; Morandotti, Roberto; Moser, Christophe; Moss, David J.; Mukherjee, Avilash; Nikdast, Mahdi; Offrein, B. J.; Oguz, Ilker; Oripov, Bakhrom; O'Shea, G.; Ozcan, Aydogan; Parmigiani, Francesca; Pasricha, Sudeep; Pavanello, Fabio; Pavesi, Lorenzo; Peserico, Nicola; Pickup, L.; Pierangeli, Davide; Pleros, Nikos; Porte, Xavier; Primavera, Bryce A.; Prucnal, Paul; Psaltis, Demetri; Puts, Lukas; Qiao, Fei; Rahmani, Babak; Raineri, Fabrice; Rios Ocampo, Carlos A.; Robertson, Joshua; Romeira, Bruno; Roques-Carmes, Charles; Rotenberg, Nir; Rowstron, A.; Schoenhardt, Steffen; Schwartz, Russell L. T.; Shainline, Jeffrey M.; Shekhar, Sudip; Skalli, A.; Sohoni, Mandar M.; Sorger, Volker J.; Soriano, Miguel C.; Spall, James; Stabile, Ripalta; Stiller, Birgit; Sunada, Satoshi; Tefas, Anastasios; Tossoun, Bassem; Tsakyridis, Apostolos; Turitsyn, Sergei K.; Van der Sande, Guy; Van Vaerenbergh, Thomas; Veraldi, Daniele; Verschaffelt, Guy; Vlieg, E. A.; Wang, Hao; Wang, Tianyu; Wetzstein, Gordon; Wright, Logan G.; Wu, Changming; Wu, Chu; Wu, Jiamin; Xia, Fei; Xu, Xingyuan; Yang, Hangbo; Yao, Weiming; Yildrim, Mustafa; Yoo, S. J.; Youngblood, Nathan; Zambrini, Roberta; Zhang, Haiou; Zhang, Weipeng
Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technology with economic and societal impact. By synthesizing contributions from over 40 research teams, this roadmap aims to provide the photonics community with a comprehensive framework for unlocking the transformative potential of PNNs in advancing AI and beyond.
