Francesco MorichettiIntroduction
For decades, information and communication technologies have exploited the properties of light to transport an ever-increasing amount of data to provide massive connectivity and broadband services. Optical fibers have replaced copper cables in long-haul transmission links, inter- and intra-datacenter communications, and fiber-to-the-home access networks. Although photonics is well-established for high-capacity transmission, its use in computing is still largely unexploited.
This is because Moore’s scaling of digital electronic processors and the use of parallel and distributed computing architectures have satisfied the need for increasingly better computer performance. However, with the advent of the deep learning era, after around 2010, computing power started doubling approximately every 4 months, which is much faster than Moore’s law Zhang and Nauman (2020); Sevilla et al. (2022) (see Figure 1). Supercomputers are rapidly approaching the exascale era, in which requirements regarding latency, bandwidth, and energy consumption are challenging for digital electronics. In addition to this, the explosive growth of artificial intelligence (AI) and machine learning, and their penetration into everyday life, is forcing us to reconsider the traditional way computers work Xu and Jin (2023), and post-Moore paradigms and computer architectures need to be seriously considered Shalf (2020). Centralized architectures are indeed inefficient in implementing models used for artificial neural networks (ANN), which are inherently distributed and require massive parallel interconnections between a multitude of elementary computing units Jain et al. (1996).
FIGURE 1. Increase of computing power [petaFLOPS per day] over the past years. Image reproduced from Zhang and Nauman (2020) under a Creative Commons licence CC BY 4.0.
Furthermore, we should consider that, even though we presume that all information is digital, “real” data, such as images, sounds, and objects, are inherently analog in their nature. If, on the one hand, digital computing does allow very complex processing, on the other hand, as the volume of data increases, digital operations become increasingly less sustainable. Today, for applications requiring massive computing, there is great interest in reconsidering analog technologies, which use dedicated circuits to efficiently process large amounts of data at very high speed Ambrogio et al. (2023).
In this scenario, photonics is emerging as a promising technology to enable the sustainable development of high-performance computing (HPC) Kitayama et al. (2019). Not only can photonics provide fast and high-capacity interconnections among distributed processors Thraskias et al. (2018), it can also perform calculations efficiently inside them Stroev and Berloff (2023). Mapping mathematical operations onto photonic hardware enables the implementation of accelerators for neuromorphic computing Shastri et al. (2021), analog optical processors Giamougiannis et al. (2023); Wu et al. (2022), and quantum integrated processors Harris et al. (2017); Corrielli et al. (2021). Several examples of programmable photonic circuits for advanced optical computing and quantum processing have been recently proposed Bogaerts et al. (2020). Multiplications and accumulations (MACs) and matrix-vector multiplications (MVMs) Zhou et al. (2022), which are the most time- and energy-consuming operations in ANNs, can be efficiently performed directly in the optical domain by exploiting diffractive free-space devices (Figure 2A) Lin et al. (2018); Mengu et al. (2020) and multi-plane light converters (MPLCs), or coherent feed-forward meshes of interferometers (Figure 2B) Shen et al. (2017); Zhang et al. (2021); Pai et al. (2023b) or non-coherent filter banks based on wavelength division multiplexing (Figure 2C) Tait et al. (2018); de Lima et al. (2019). Such accelerators are expected to enable processing speeds of petaMAC operations per second per mm2 and energy efficiencies of attojoule per MAC Nahmias et al. (2020); Nozaki et al. (2019). Recursive programmable photonic circuits with optical feedback structures have been proposed as mathematical co-processors and reconfigurable solvers of several classes of integral/differential equations (Figure 2D) Tzarouchis et al. (2022).
FIGURE 2. Examples of photonic architectures performing computation in the optical domain. (A) Diffractive deep neural network trained for all-optical classification of handwritten digits: the diffractive optical surfaces are trained to provide amplitude and phase spatial modulation on a transmitted optical beam in order to implement cascaded artificial neurons Mengu et al. (2020). (B) Programmable nanophotonic processor made of cascaded integrated Mach Zehender interferometers realizing weight matrix multiplication in an photonic NN Zhang et al. (2021); (C) Microring resonator (MRR) weight banks providing non-coherent weighted additions in photonic NNs: incoherent wavelength division multiplexed (WDM) optical signals are summed by balanced photodetectors, whose current represent the sum of the WDM weigthed by the MRRs Tait et al. (2018). (D) Schematic of a recursive programmable photonic structure with optical feed-back loop solving linear integral and differential equations, and performing matrix inversion Tzarouchis et al. (2022). Panels (A,B and D) reproduced under a Creative Commons licence CC BY 4.0. Panel (C) reprinted with permission from Tait et al. (2018) © Optical Society of America.
To make these proof-of-concept photonic devices evolve toward mature large-scale computing systems, research in “neuromorphic photonics and photonic computing” needs to tackle several open challenges that vertically span from the investigation of novel material properties up to the optimization of computing networks:
• Energy sustainability. To dramatically reduce power dissipation in photonic computers that can be programmed and trained with “quasi-zero-energy-consumption”, technology breakthroughs are required to implement fast and energy-efficient optical actuators. Recently, several advances have been demonstrated by exploiting fast electro-optic and plasmonic weighting banks in photonic ANNs Dabos et al. (2022) as well as non-volatile switchable elements based on phase-change materials Figure 3A Miscuglio and Sorger (2020); Feldmann et al. (2019); Farmakidis et al. (2022), optomechanical devices Quack et al. (2023), optical memristors Farmakidis et al. (2022); Youngblood et al. (2023), and liquid crystal devices Yin et al. (2022).
• More functionalities. Novel devices are required to enlarge the portfolio of signal processing operations that can be operated directly in the optical domain, both in free-space optics and guided wave devices. Here, promising device concepts are emerging, which are enabled by the realization of tuneable meta-surfaces, subwavelength meta-structures, and meta-devices engineered at the nanoscale Figure 3B Cui et al. (2014); Halir et al. (2015); Li et al. (2022).
• Scalability. Today, photonic integrated circuits (PICs) can reach very high-integration densities enabled by complementary-metal-oxide-semiconductor (CMOS) manufacturing, but scalability to complex on-chip architectures requires sophisticated modeling and design techniques as well as advanced algorithms, control techniques, and training tools for automated calibration and adaptive reconfiguration Figure 3C Hughes et al. (2018); Milanizadeh et al. (2020); Filipovich et al. (2022); Pai et al. (2023b).
• Non-linear processing and data storage. Such operations, which are essential in neuromorphic computing and AI, are still inefficient when operated in optics with respect to the performance offered by electronics; in this view, the integration of photonics and electronic circuits may offer a viable route to profit from the main advantages offered by the two technological platforms. However, this approach requires reconsideration of the way integrated circuits are realized, developing suitable procedures for co-designing and co-packaging photonics and electronics chips Peserico et al. (2022).
• Electronic-photonic co-integration. The integration of analog photonic elements into analog/digital electronic circuits could herald new classes of computing devices combining the best of both worlds. However, such interplay opens new issues related to the co-design, co-integration, and co-packaging of photonic and electronic circuits. Monolithic integration Figure 3D Atabaki et al. (2018); Zanetto et al. (2023), heterogeneous integration, and chip-to-chip bonding Chang et al. (2023); Nezami et al. (2023) are different approaches that are being explored to leverage the potential of photonic computing.
FIGURE 3. (A) Phase-change optical memristors: a plasmonic device exploits narrowly spaced lectrodes integrated in optical waveguides to provide strong optical and electrical coupling and enable highly energy-efficient modulation of the light Farmakidis et al. (2022). (B) Programmable metasurface made of square meta-atoms that can by digitally controlled to realize ‘0’ (no phase shift) and ‘1’ (π phase shift) elements and arbitrarily control the scattering of optical beams Cui et al. (2014); (C) Schematic of an ANN architecture that exploit meshes of MZIs interleaved with nonlinear activation functions: gradient computation in the ANN is performend by implementing a photonic analogue of the backpropagation algorithm and enables highly efficient, in situ training of a photonic neural network Hughes et al. (2018). (D). Zero-change integration of electronics into a silicon photonic platform: schematic of the technological platform and of a MOSFET device Zanetto et al. (2023). Panels (A,B and D) reproduced under a Creative Commons licence CC BY 4.0. Panel C reprinted with permission from (Hughes et al. (2018)) © Optical Society of America.
Applications for photonic computing are envisioned in mathematical accelerators for deep learning Kitayama et al. (2019); Zhou et al. (2022), photonic reservoir computing Nakajima et al. (2021), and spiking neural networks Feldmann et al. (2019); cognitive radio Zhu and Pan (2020) and compressive sensing Kilic et al. (2023); the classification of unstructured signals, high-energy particle collision Duarte et al. (2018), and qubit systems Magesan et al. (2015); the non-linear control and optimization in robotics and autonomous vehicles Kuutti et al. (2021); intelligent signal processing in wireless and fibre-optic communication Huang et al. (2022); Milanizadeh et al. (2022); and optical quantum key distribution Kwek et al. (2021) and chaotic systems and encryption Pai et al. (2023a).
Although photonic computing is still in its infancy, in recent years, we have assisted an impressive increase in fundamental and applied research in this field, which is driven by large industrial investment and the birth of small- and medium-scale enterprises. This means that photonic computing is not just a fascinating scientific area but its potential is critically strategic for stakeholders to drive investments in those areas that might have the highest impact, such as AI and quantum information. Photonic computing will probably never replace conventional computers but it can offer real opportunities for a sustainable scalability of performance in distributed and edge-computing systems. Indeed, without photonics, we may have no other alternative technologies to address the bottleneck that electronics are bound to face in meeting the requirements of future computing. Miller (2017).
Author contributions
FM: Writing–original draft.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. FM acknowledges financial support from the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on ‘Telecommunications of the Future’ (PE00000001—program ‘RESTART’, Structural Project ‘Rigoletto’ and Focused Project ‘HePIC’).
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Ambrogio, S., Narayanan, P., Okazaki, A., Fasoli, A., Mackin, C., Hosokawa, K., et al. (2023). An analog-ai chip for energy-efficient speech recognition and transcription. Nature 620, 768–775. doi:10.1038/s41586-023-06337-5
Atabaki, A. H., Moazeni, S., Pavanello, F., Gevorgyan, H., Notaros, J., Alloatti, L., et al. (2018). Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature 556, 349–354. doi:10.1038/s41586-018-0028-z
Bogaerts, W., Pérez, D., Capmany, J., Miller, D. A. B., Poon, J., Englund, D., et al. (2020). Programmable photonic circuits. Nature 586, 207–216. doi:10.1038/s41586-020-2764-0
Chang, P.-H., Samanta, A., Yan, P., Fu, M., Zhang, Y., On, M. B., et al. (2023). A 3d integrated energy-efficient transceiver realized by direct bond interconnect of co-designed 12 nm finfet and silicon photonic integrated circuits. J. Light. Technol. 41, 6741–6755. doi:10.1109/jlt.2023.3291704
Corrielli, G., Crespi, A., and Osellame, R. (2021). Femtosecond laser micromachining for integrated quantum photonics. Nanophotonics 10, 3789–3812. doi:10.1515/nanoph-2021-0419
Cui, T. J., Qi, M. Q., Wan, X., Zhao, J., and Cheng, Q. (2014). Coding metamaterials, digital metamaterials and programmable metamaterials. Light Sci. Appl. 3, e218. doi:10.1038/lsa.2014.99
Dabos, G., Bellas, D. V., Stabile, R., Moralis-Pegios, M., Giamougiannis, G., Tsakyridis, A., et al. (2022). Neuromorphic photonic technologies and architectures: scaling opportunities and performance frontiers [Invited]. Opt. Mat. Express 12, 2343–2367. doi:10.1364/OME.452138
de Lima, T. F., Peng, H.-T., Tait, A. N., Nahmias, M. A., Miller, H. B., Shastri, B. J., et al. (2019). Machine learning with neuromorphic photonics. J. Light. Technol. 37, 1515–1534. doi:10.1109/JLT.2019.2903474
Duarte, J., Han, S., Harris, P., Jindariani, S., Kreinar, E., Kreis, B., et al. (2018). Fast inference of deep neural networks in fpgas for particle physics. J. Instrum. 13, P07027. doi:10.1088/1748-0221/13/07/P07027
Farmakidis, N., Youngblood, N., Lee, J. S., Feldmann, J., Lodi, A., Li, X., et al. (2022). Electronically reconfigurable photonic switches incorporating plasmonic structures and phase change materials. Adv. Sci. 9, 2200383. doi:10.1002/advs.202200383
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H., and Pernice, W. H. P. (2019). All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214. doi:10.1038/s41586-019-1157-8
Filipovich, M. J., Guo, Z., Al-Qadasi, M., Marquez, B. A., Morison, H. D., Sorger, V. J., et al. (2022). Silicon photonic architecture for training deep neural networks with direct feedback alignment. Optica 9, 1323–1332. doi:10.1364/OPTICA.475493
Giamougiannis, G., Tsakyridis, A., Moralis-Pegios, M., Pappas, C., Kirtas, M., Passalis, N., et al. (2023). Analog nanophotonic computing going practical: silicon photonic deep learning engines for tiled optical matrix multiplication with dynamic precision. Nanophotonics 12, 963–973. doi:10.1515/nanoph-2022-0423
Halir, R., Bock, P. J., Cheben, P., Ortega-Moñux, A., Alonso-Ramos, C., Schmid, J. H., et al. (2015). Waveguide sub-wavelength structures: a review of principles and applications. Laser and Photonics Rev. 9, 25–49. doi:10.1002/lpor.201400083
Harris, N. C., Steinbrecher, G. R., Prabhu, M., Lahini, Y., Mower, J., Bunandar, D., et al. (2017). Quantum transport simulations in a programmable nanophotonic processor. Nat. Photonics 11, 447–452. doi:10.1038/nphoton.2017.95
Huang, C., Sorger, V. J., Miscuglio, M., Al-Qadasi, M., Mukherjee, A., Lampe, L., et al. (2022). Prospects and applications of photonic neural networks. Adv. Phys. X 7, 1981155. doi:10.1080/23746149.2021.1981155
Hughes, T. W., Minkov, M., Shi, Y., and Fan, S. (2018). Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica 5, 864–871. doi:10.1364/OPTICA.5.000864
Jain, A., Mao, J., and Mohiuddin, K. (1996). Artificial neural networks: a tutorial. Computer 29, 31–44. doi:10.1109/2.485891
Kilic, V., Tran, T. D., and Foster, M. A. (2023). Compressed sensing in photonics: tutorial. J. Opt. Soc. Am. B 40, 28–52. doi:10.1364/JOSAB.469865
Kitayama, K.-i., Notomi, M., Naruse, M., Inoue, K., Kawakami, S., and Uchida, A. (2019). Novel frontier of photonics for data processing—photonic accelerator. Apl. Photonics 4, 090901. doi:10.1063/1.5108912
Kuutti, S., Bowden, R., Jin, Y., Barber, P., and Fallah, S. (2021). A survey of deep learning applications to autonomous vehicle control. IEEE Trans. Intelligent Transp. Syst. 22, 712–733. doi:10.1109/TITS.2019.2962338
Kwek, L.-C., Cao, L., Luo, W., Wang, Y., Sun, S., Wang, X., et al. (2021). Chip-based quantum key distribution. AAPPS Bull. 31, 15–4710. doi:10.1007/s43673-021-00017-0
Li, L., Zhao, H., Liu, C., Li, L., and Cui, T. J. (2022). Intelligent metasurfaces: control, communication and computing. eLight 2, 7–8643. doi:10.1186/s43593-022-00013-3
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., et al. (2018). All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008. doi:10.1126/science.aat8084
Magesan, E., Gambetta, J. M., Córcoles, A. D., and Chow, J. M. (2015). Machine learning for discriminating quantum measurement trajectories and improving readout. Phys. Rev. Lett. 114, 200501. doi:10.1103/PhysRevLett.114.200501
Mengu, D., Zhao, Y., Yardimci, N. T., Rivenson, Y., Jarrahi, M., and Ozcan, A. (2020). Misalignment resilient diffractive optical networks. Nanophotonics 9, 4207–4219. doi:10.1515/nanoph-2020-0291
Milanizadeh, M., Ahmadi, S., Petrini, M., Aguiar, D., Mazzanti, R., Zanetto, F., et al. (2020). Control and calibration recipes for photonic integrated circuits. IEEE J. Sel. Top. Quantum Electron. 26, 1–10. doi:10.1109/JSTQE.2020.2975657
Milanizadeh, M., SeyedinNavadeh, S., Zanetto, F., Grimaldi, V., Vita, C. D., Klitis, C., et al. (2022). Separating arbitrary free-space beams with an integrated photonic processor. Light Sci. Appl. 11, 197. doi:10.1038/s41377-022-00884-8
Miller, D. A. B. (2017). Attojoule optoelectronics for low-energy information processing and communications. J. Light. Technol. 35, 346–396. doi:10.1109/JLT.2017.2647779
Miscuglio, M., and Sorger, V. J. (2020). Photonic tensor cores for machine learning. Appl. Phys. Rev. 7, 031404. doi:10.1063/5.0001942
Nahmias, M. A., de Lima, T. F., Tait, A. N., Peng, H.-T., Shastri, B. J., and Prucnal, P. R. (2020). Photonic multiply-accumulate operations for neural networks. IEEE J. Sel. Top. Quantum Electron. 26, 1–18. doi:10.1109/JSTQE.2019.2941485
Nakajima, M., Tanaka, K., and Hashimoto, T. (2021). Scalable reservoir computing on coherent linear photonic processor. Commun. Phys. 4, 20–3650. doi:10.1038/s42005-021-00519-1
Nezami, M. S., de Lima, T. F., Mitchell, M., Yu, S., Wang, J., Bilodeau, S., et al. (2023). Packaging and interconnect considerations in neuromorphic photonic accelerators. IEEE J. Sel. Top. Quantum Electron. 29, 1–11. doi:10.1109/JSTQE.2022.3200604
Nozaki, K., Matsuo, S., Fujii, T., Takeda, K., Shinya, A., Kuramochi, E., et al. (2019). Femtofarad optoelectronic integration demonstrating energy-saving signal conversion and nonlinear functions. Nat. Photonics 13, 454–459. doi:10.1038/s41566-019-0397-3
Pai, S., Park, T., Ball, M., Penkovsky, B., Dubrovsky, M., Abebe, N., et al. (2023a). Experimental evaluation of digitally verifiable photonic computing for blockchain and cryptocurrency. Optica 10, 552–560. doi:10.1364/OPTICA.476173
Pai, S., Sun, Z., Hughes, T. W., Park, T., Bartlett, B., Williamson, I. A. D., et al. (2023b). Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science 380, 398–404. doi:10.1126/science.ade8450
Peserico, N., de Lima, T. F., Prucnal, P., and Sorger, V. J. (2022). Emerging devices and packaging strategies for electronic-photonic ai accelerators: opinion. Opt. Mat. Express 12, 1347–1351. doi:10.1364/OME.451802
Quack, N., Takabayashi, A. Y., Sattari, H., Edinger, P., Jo, G., Bleiker, S. J., et al. (2023). Integrated silicon photonic mems. Microsystems Nanoeng. 9, 27–7434. doi:10.1038/s41378-023-00498-z
Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., and Villalobos, P. (2022). “Compute trends across three eras of machine learning,” in 2022 International Joint Conference on Neural Networks (IJCNN), 1–8. doi:10.1109/IJCNN55064.2022.9891914
Shalf, J. (2020). The future of computing beyond Moore’s Law. Philosophical Trans. R. Soc. A Math. Phys. Eng. Sci. 378, 20190061. doi:10.1098/rsta.2019.0061
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., et al. (2021). Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15, 102–114. doi:10.1038/s41566-020-00754-y
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., et al. (2017). Deep learning with coherent nanophotonic circuits. Nat. Photonics 11, 441–446. doi:10.1038/nphoton.2017.93
Stroev, N., and Berloff, N. G. (2023). Analog photonics computing for information processing, inference, and optimization. Adv. Quantum Technol. 6, 2300055. doi:10.1002/qute.202300055
Tait, A. N., Jayatilleka, H., Lima, T. F. D., Ma, P. Y., Nahmias, M. A., Shastri, B. J., et al. (2018). Feedback control for microring weight banks. Opt. Express 26, 26422–26443. doi:10.1364/OE.26.026422
Thraskias, C. A., Lallas, E. N., Neumann, N., Schares, L., Offrein, B. J., Henker, R., et al. (2018). Survey of photonic and plasmonic interconnect technologies for intra-datacenter and high-performance computing communications. IEEE Commun. Surv. Tutorials 20, 2758–2783. doi:10.1109/COMST.2018.2839672
Tzarouchis, D. C., Mencagli, M. J., Edwards, B., and Engheta, N. (2022). Mathematical operations and equation solving with reconfigurable metadevices. Light Sci. Appl. 11, 263. doi:10.1038/s41377-022-00950-1
Wu, J., Lin, X., Guo, Y., Liu, J., Fang, L., Jiao, S., et al. (2022). Analog optical computing for artificial intelligence. Engineering 10, 133–145. doi:10.1016/j.eng.2021.06.021
Xu, X.-Y., and Jin, X.-M. (2023). Integrated photonic computing beyond the von neumann architecture. ACS Photonics 10, 1027–1036. doi:10.1021/acsphotonics.2c01543
Yin, K., Hsiang, E.-L., Zou, J., Li, Y., Yang, Z., Yang, Q., et al. (2022). Advanced liquid crystal devices for augmented reality and virtual reality displays: principles and applications. Light Sci. Appl. 11, 161. doi:10.1038/s41377-022-00851-3
Youngblood, N., Ríos Ocampo, C. A., Pernice, W. H. P., and Bhaskaran, H. (2023). Integrated optical memristors. Nat. Photonics 17, 561–572. doi:10.1038/s41566-023-01217-w
Zanetto, F., Toso, F., Grimaldi, V., Petrini, M., Martinez, A., Milanizadeh, M., et al. (2023). Time-multiplexed control of programmable silicon photonic circuits enabled by monolithic cmos electronics. Laser and Photonics Rev. 17, 2300124. doi:10.1002/lpor.202300124
Zhang, H., Gu, M., Jiang, X. D., Thompson, J., Cai, H., Paesani, S., et al. (2021). An optical neural chip for implementing complex-valued neural network. Nat. Commun. 12, 457. doi:10.1038/s41467-020-20719-7
Zhang, Y., and Nauman, U. (2020). Deep learning trends driven by temes: a philosophical perspective. IEEE Access 8, 196587–196599. doi:10.1109/ACCESS.2020.3032143
Zhou, H., Dong, J., Cheng, J., Dong, W., Huang, C., Shen, Y., et al. (2022). Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl. 11, 30. doi:10.1038/s41377-022-00717-8
Keywords: photonic computing, photonic integrated circuits (PICs), photonic neural networks, photonic quantum computing, analog computing, photonics, neuromorphic photonics
Citation: Morichetti F (2024) Grand challenges in neuromorphic photonics and photonic computing. Front. Photonics 4:1336510. doi: 10.3389/fphot.2023.1336510
Received: 10 November 2023; Accepted: 30 December 2023;
Published: 29 January 2024.
Edited and reviewed by:
Marco Peccianti, Loughborough University, United KingdomCopyright © 2024 Morichetti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Francesco Morichetti, francesco.morichetti@polimi.it