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arxiv: 2604.12893 · v1 · submitted 2026-04-14 · ⚛️ physics.optics

Neuron Surface Emitting Laser (NeuronSEL): Spiking Regimes and Negative Differential Resistance in Solitary Multi-junction VCSELs

Pith reviewed 2026-05-10 14:28 UTC · model grok-4.3

classification ⚛️ physics.optics
keywords neuromorphic photonicsVCSELspiking neuronsnegative differential resistanceoptical computingintegrate-and-firecoincidence detection
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The pith

A single multi-junction VCSEL produces optical spikes and neural-like computations through its own negative differential resistance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that a compact, solitary multi-junction vertical-cavity surface-emitting laser can generate negative differential resistance without any external circuitry. This built-in nonlinearity produces optical spiking signals that show refractoriness after firing and threshold-based integrate-and-fire dynamics. The same device then performs simple processing tasks such as coincidence detection and exclusive-or logic using its light output. Because the structure is already mass-producible and array-friendly, the result points to a practical route for scalable optical neuromorphic hardware.

Core claim

The Neuron Surface-Emitting Laser, a single-stack multi-junction VCSEL, exhibits non-linear negative differential resistance under solitary operation. This property directly enables optical spiking emission with neuronal features including refractoriness and integrate-and-fire behavior, allowing the device to carry out coincidence detection and exclusive-or operations as an optical spiking neuron.

What carries the argument

Negative differential resistance arising intrinsically from the multi-junction VCSEL structure, which produces the non-linear electrical response that drives spiking optical emission.

If this is right

  • The device functions as an optical spiking neuron that exhibits refractoriness and threshold-based integrate-and-fire dynamics.
  • It performs coincidence detection and exclusive-or logic using only its optical spiking output.
  • Arrays of NeuronSELs can be arranged into networks that execute classification tasks.
  • The approach inherits VCSEL advantages of low cost, compactness, vertical emission, and straightforward scaling into large arrays.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Integration into photonic circuits could eliminate separate electronic driver stages for each neuron.
  • Dense vertical arrays might support higher interconnection density than planar electronic neuromorphic chips.
  • Real-time optical sensing applications could exploit the same device for both light emission and decision-making.
  • Stability testing in fabricated arrays would be needed to confirm that spiking persists without external tuning.

Load-bearing premise

The negative differential resistance and resulting spiking arise directly from the multi-junction design itself when the laser runs alone, without external controls or fabrication variations.

What would settle it

Fabricate and measure the current-voltage curve of an isolated multi-junction VCSEL to determine whether a stable negative-resistance region appears and consistently produces spiking output without added circuitry.

read the original abstract

Neuromorphic photonics is emerging as a powerful platform for fast and efficient optical information processing and sensing. However, future brain-inspired photonic systems require compact and scalable light sources, capable of generating the neuro-mimetic optical signals needed for their operation. This work demonstrates a single-stack laser that delivers optical and electrical neural-like spiking emission under solitary operation. Termed the Neuron Surface-Emitting Laser (NeuronSEL), this compact, multi-junction Vertical-Cavity Surface Emitting Laser (VCSEL) exhibits non-linear Negative Differential Resistance (NDR), similar to that observed in memristive devices. Leveraging this NDR behaviour enables the novel demonstration of multiple neuronal features in the NeuronSEL including refractoriness and threshold-/integrate-and-fire dynamics. We demonstrate the NeuronSEL's behaviour as an optical spiking neuron and its ability to perform processing functions, such as coincidence detection and exclusive OR operations. Its scalability is illustrated by proposing a network based on an array of NeuronSELs, able to perform classification tasks. The NeuronSEL emerges as a strong candidate for practical and scalable neuromorphic photonic hardware, with potential impact across a range of applications in optical sensing, communications and computing technologies, whilst benefitting from the inherent advantages of VCSEL technology -low manufacturing cost, compactness, efficiency, vertical emission, and straightforward integration into large arrayed-structures and networks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces the NeuronSEL, a single-stack multi-junction VCSEL that exhibits negative differential resistance (NDR) under solitary voltage bias and uses this to demonstrate optical spiking with neuronal features including refractoriness, threshold and integrate-and-fire dynamics, coincidence detection, and XOR operations. It further proposes an array-based network for classification tasks, positioning the device as a scalable platform for neuromorphic photonics leveraging standard VCSEL advantages.

Significance. If the NDR and spiking behaviors are confirmed to arise intrinsically from the multi-junction structure under solitary operation without external circuit dependencies, the work would offer a compact, low-cost optical spiking neuron compatible with existing VCSEL fabrication and array integration. The explicit demonstrations of processing functions and the array proposal for classification add practical relevance to neuromorphic photonics, though the absence of quantitative metrics in the provided abstract limits immediate assessment of robustness.

major comments (2)
  1. [§4, Figure 2] §4 (Device Characterization and I-V Measurements), Figure 2: The NDR region is presented as intrinsic to the solitary multi-junction VCSEL, but the measurement protocol does not specify series resistance, load resistor, or biasing network details. In VCSEL literature, apparent NDR often originates from external elements rather than tunneling or carrier dynamics; without this clarification the claim that spiking regimes emerge under purely solitary bias cannot be evaluated.
  2. [§5, Figures 4-6] §5 (Spiking Regimes and Neuronal Dynamics), Figures 4-6: The demonstrations of refractoriness, integrate-and-fire behavior, coincidence detection, and XOR operation lack reported input pulse amplitudes, repetition rates, error bars, or control experiments (e.g., single-junction VCSEL comparison or open-circuit conditions). These omissions make it impossible to confirm that the observed optical spikes are device-intrinsic rather than circuit-mediated.
minor comments (2)
  1. [Abstract] The abstract is overly dense; splitting the claims into separate sentences would improve readability.
  2. [Introduction] No equations or fitted parameters are used, which is appropriate for an experimental report, but the text should explicitly state the absence of any free parameters in the interpretation of NDR.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive comments on our manuscript. We have carefully considered each point and provide our responses below. Where appropriate, we have revised the manuscript to incorporate additional details and clarifications that address the referee's concerns.

read point-by-point responses
  1. Referee: [§4, Figure 2] §4 (Device Characterization and I-V Measurements), Figure 2: The NDR region is presented as intrinsic to the solitary multi-junction VCSEL, but the measurement protocol does not specify series resistance, load resistor, or biasing network details. In VCSEL literature, apparent NDR often originates from external elements rather than tunneling or carrier dynamics; without this clarification the claim that spiking regimes emerge under purely solitary bias cannot be evaluated.

    Authors: We agree that the measurement protocol was not described in sufficient detail in the original manuscript, which is necessary to unambiguously establish the intrinsic origin of the NDR. We have revised §4 to provide a full account of the biasing conditions, including the use of a source meter with direct probing and minimal external resistance. We explicitly state that the NDR is due to the multi-junction structure and not external circuit elements. Additionally, we have included a comparison measurement with a single-junction VCSEL that exhibits no NDR under identical conditions. These revisions are reflected in the updated text and Figure 2. revision: yes

  2. Referee: [§5, Figures 4-6] §5 (Spiking Regimes and Neuronal Dynamics), Figures 4-6: The demonstrations of refractoriness, integrate-and-fire behavior, coincidence detection, and XOR operation lack reported input pulse amplitudes, repetition rates, error bars, or control experiments (e.g., single-junction VCSEL comparison or open-circuit conditions). These omissions make it impossible to confirm that the observed optical spikes are device-intrinsic rather than circuit-mediated.

    Authors: The referee is correct that the original presentation lacked several key experimental parameters and controls. In the revised manuscript, we now specify the amplitudes and repetition rates of the input optical pulses used in the experiments, along with error bars on the plotted data in Figures 4-6. We have also added control experiments comparing the NeuronSEL to single-junction VCSELs (which do not show spiking) and measurements in open-circuit conditions to demonstrate that the spiking behavior is intrinsic to the device rather than arising from external circuitry. These additions are incorporated into §5 and the associated figures. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental device report with no derivations or fitted parameters

full rationale

The paper presents an experimental characterization of a multi-junction VCSEL device, reporting observed NDR and spiking behaviors under solitary bias. It contains no equations, derivations, parameter fits, or load-bearing self-citations that could reduce claims to inputs by construction. All results are direct measurements of device physics, with no ansatz, uniqueness theorem, or renamed empirical pattern invoked. The work is therefore self-contained as an empirical demonstration against external benchmarks, with no circular steps present.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work is an experimental demonstration of device behavior; the abstract contains no mathematical model, free parameters, axioms, or invented theoretical entities.

pith-pipeline@v0.9.0 · 5586 in / 1014 out tokens · 30949 ms · 2026-05-10T14:28:59.888118+00:00 · methodology

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Reference graph

Works this paper leans on

50 extracted references · 50 canonical work pages

  1. [1]

    & Grollier, J

    Markovi´ c, D., Mizrahi, A., Querlioz, D. & Grollier, J. Physics for neuromorphic computing.Nature Reviews Physics2, 499–510 (2020)

  2. [2]

    V.et al.2022 roadmap on neuromorphic computing and engineering.Neuromorphic Computing and Engineering2, 022501 (2022)

    Christensen, D. V.et al.2022 roadmap on neuromorphic computing and engineering.Neuromorphic Computing and Engineering2, 022501 (2022)

  3. [3]

    J.et al.Photonics for artificial intelligence and neuromorphic computing.Nature Photonics15, 102–114 (2021)

    Shastri, B. J.et al.Photonics for artificial intelligence and neuromorphic computing.Nature Photonics15, 102–114 (2021)

  4. [4]

    Brunner, D.et al.Roadmap on neuromorphic photonics.arXiv preprint arXiv:2501.07917(2025). 14

  5. [5]

    URL https://iopscience.iop.org/article/10.1088/1674-4926/42/2/023105

    Xiang, S.et al.A review: Photonics devices, architectures, and algorithms for optical neural computing.Journal of Semiconductors42, 023105 (2021). URL https://iopscience.iop.org/article/10.1088/1674-4926/42/2/023105

  6. [6]

    Ashtiani, F., Geers, A. J. & Aflatouni, F. An on-chip photonic deep neural network for image classification.Nature606, 501–506 (2022). URL https://www. nature.com/articles/s41586-022-04714-0

  7. [7]

    URL https://www

    Giamougiannis, G.et al.Neuromorphic silicon photonics with 50 GHz tiled matrix multiplication for deep-learning applica- tions.Advanced Photonics5(2023). URL https://www. spiedigitallibrary.org/journals/advanced-photonics/volume-5/issue-01/016004/ Neuromorphic-silicon-photonics-with-50GHz-tiled-matrix-multiplication-for-deep/ 10.1117/1.AP.5.1.016004.full

  8. [8]

    URL https://spj.science.org/doi/10

    Biasi, S.et al.Photonic Neural Networks Based on Integrated Silicon Microres- onators.Intelligent Computing3(2024). URL https://spj.science.org/doi/10. 34133/icomputing.0067

  9. [9]

    URL https://ieeexplore.ieee.org/document/ 10835188/

    Tossoun, B.et al.Large-Scale Integrated Photonic Device Platform for Energy-Efficient AI/ML Accelerators.IEEE Journal of Selected Topics in Quan- tum Electronics31, 1–26 (2025). URL https://ieeexplore.ieee.org/document/ 10835188/

  10. [10]

    D., Bhaskaran, H

    Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities.Nature 569, 208–214 (2019). URL http://www.nature.com/articles/s41586-019-1157-8

  11. [11]

    K.et al.Training spiking neural networks using lessons from deep learning.Proceedings of the IEEE111, 1016–1054 (2023)

    Eshraghian, J. K.et al.Training spiking neural networks using lessons from deep learning.Proceedings of the IEEE111, 1016–1054 (2023)

  12. [12]

    Owen-Newns, D.et al.Photonic spiking neural network built with a single vcsel for high-speed time series prediction.Communications physics8, 110 (2025)

  13. [13]

    & Pfeiffer, M

    O’Connor, P., Neil, D., Liu, S.-C., Delbruck, T. & Pfeiffer, M. Real-time clas- sification and sensor fusion with a spiking deep belief network.Frontiers in neuroscience7, 178 (2013)

  14. [14]

    Zhou, Y.et al.Computational event-driven vision sensors for in-sensor spiking neural networks.Nature Electronics6, 870–878 (2023)

  15. [15]

    & Chai, Y

    Zhou, F. & Chai, Y. Near-sensor and in-sensor computing.Nature Electronics3, 664–671 (2020). URL https://www.nature.com/articles/s41928-020-00501-9

  16. [16]

    URL https://spj.science.org/doi/10

    Biasi, S.et al.Photonic Neural Networks Based on Integrated Silicon Microres- onators.Intelligent Computing3(2024). URL https://spj.science.org/doi/10. 34133/icomputing.0067. 15

  17. [17]

    & Pavesi, L

    Borghi, M., Biasi, S. & Pavesi, L. Reservoir computing based on a silicon micror- ing and time multiplexing for binary and analog operations.Scientific Reports 11, 15642 (2021). URL https://www.nature.com/articles/s41598-021-94952-5

  18. [18]

    & Hurtado, A

    Donati, G., Biasi, S., Pavesi, L. & Hurtado, A. All-optical spiking processing and reservoir computing with a passive silicon microring and wavelength-time division multiplexing.Photonics Research13, 2641 (2025). URL https://opg.optica.org/ abstract.cfm?URI=prj-13-9-2641

  19. [19]

    R´ ıos, C.et al.In-memory computing on a photonic platform.Science advances 5, eaau5759 (2019)

  20. [20]

    Feldmann, J.et al.Parallel convolutional processing using an integrated photonic tensor core.Nature589, 52–58 (2021)

  21. [21]

    & Zou, W

    Xu, S., Wang, J., Wang, R., Chen, J. & Zou, W. High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays.Optics express27, 19778–19787 (2019)

  22. [22]

    URL https://opg.optica.org/abstract.cfm?URI= optica-7-5-551

    Prabhu, M.et al.Accelerating recurrent Ising machines in photonic integrated circuits.Optica7, 551 (2020). URL https://opg.optica.org/abstract.cfm?URI= optica-7-5-551

  23. [23]

    URL https://www

    Pai, S.et al.Experimentally realized in situ backpropagation for deep learning in photonic neural networks.Science380, 398–404 (2023). URL https://www. science.org/doi/10.1126/science.ade8450

  24. [24]

    URL https://advanced.onlinelibrary.wiley.com/doi/10.1002/ adma.202508029

    Wang, G.et al.Integrated Neuromorphic Photonic Computing for AI Accelera- tion: Emerging Devices, Network Architectures, and Future Paradigms.Advanced Materials(2025). URL https://advanced.onlinelibrary.wiley.com/doi/10.1002/ adma.202508029

  25. [25]

    URL https://pubs.aip.org/app/article/9/7/070903/3304156/ Semiconductor-lasers-for-photonic-neuromorphic

    Xiang, S.et al.Semiconductor lasers for photonic neuromorphic com- puting and photonic spiking neural networks: A perspective.APL Pho- tonics9(2024). URL https://pubs.aip.org/app/article/9/7/070903/3304156/ Semiconductor-lasers-for-photonic-neuromorphic

  26. [26]

    Compact incoherent image differentiation with nanophotonic structures

    Huang, Y.et al.Harnessing Graded-like Spiking Dynamics in Semiconductor Lasers for High-Speed and Energy-Efficient Reservoir Computing.ACS Pho- tonics13, 433–444 (2026). URL https://pubs.acs.org/doi/10.1021/acsphotonics. 5c02170

  27. [27]

    URL https://link.aps.org/doi/10.1103/n41r-3t9v

    Donati, G.et al.Spiking rate and latency encoding with resonant tunneling diode neuron circuits and design influences.Physical Review Applied24, 024041 (2025). URL https://link.aps.org/doi/10.1103/n41r-3t9v. 16

  28. [28]

    URL https://advanced.onlinelibrary.wiley.com/doi/ 10.1002/aisy.202500800

    Owen-Newns, D.et al.Neuromorphic Photonic Processing and Memory With Spiking Resonant Tunneling Diode Neurons and Neural Networks.Advanced Intelligent Systems(2026). URL https://advanced.onlinelibrary.wiley.com/doi/ 10.1002/aisy.202500800

  29. [29]

    URL https://doi.org/10.1515/nanoph-2022-0362

    Hejda, M.et al.Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser.Nanophotonics 12, 857–867 (2023). URL https://doi.org/10.1515/nanoph-2022-0362

  30. [30]

    URL https: //advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202000085

    Mehonic, A.et al.Memristors—From In-Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio-Inspired Computing.Advanced Intelligent Systems2(2020). URL https: //advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202000085

  31. [31]

    URL https://www.nature.com/articles/s41467-017-00773-4

    Kumar, S.et al.Physical origins of current and temperature controlled negative differential resistances in NbO2.Nature Communications8, 658 (2017). URL https://www.nature.com/articles/s41467-017-00773-4

  32. [32]

    & Guo, X

    Peng, H., Gan, L. & Guo, X. Memristor-based spiking neural networks: coop- erative development of neural network architecture/algorithms and memristors. Chip3, 100093 (2024)

  33. [33]

    URL https://www.nature.com/articles/s41467-024-55293-9

    Pei, Y.et al.Ultra robust negative differential resistance memristor for hardware neuron circuit implementation.Nature Communications16, 48 (2025). URL https://www.nature.com/articles/s41467-024-55293-9

  34. [34]

    Skalli, A.et al.Photonic neuromorphic computing using vertical cavity semicon- ductor lasers.Optical Materials Express12, 2395–2414 (2022)

  35. [35]

    & Lott, J

    Haghighi, N. & Lott, J. A. Electrically parallel three-element 980 nm vcsel arrays with ternary and binary bottom dbr mirror layers.Materials14, 397 (2021)

  36. [36]

    URL https://www.nature.com/articles/s41377-024-01561-8

    Pan, G.et al.Harnessing the capabilities of VCSELs: unlocking the potential for advanced integrated photonic devices and systems.Light: Science & Applications 13, 229 (2024). URL https://www.nature.com/articles/s41377-024-01561-8

  37. [37]

    and Reitzenstein, Stephan and Hamerly, Ryan and Englund, Dirk , doi =

    Chen, Z.et al.Deep learning with coherent VCSEL neural networks.Nature Photonics17, 723–730 (2023). URL http://arxiv.org/abs/2207.05329

  38. [38]

    Staudinger, P.et al.Zediker, M. S. & Zucker, E. P. (eds)Multi- junction lasers for LiDAR applications. (eds Zediker, M. S. & Zucker, E. P.)High-Power Diode Laser Technology XXI, 3 (SPIE, 2023). URL https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12403/ 2655771/Multi-junction-lasers-for-LiDAR-applications/10.1117/12.2655771.full

  39. [39]

    URL https://ieeexplore.ieee.org/document/10691944/

    Zhou, X.et al.Thermal Properties of Multi-Junction Cascade Vertical Cavity Surface Emitting Lasers.IEEE Transactions on Electron Devices71, 6831–6837 17 (2024). URL https://ieeexplore.ieee.org/document/10691944/

  40. [40]

    URL https://www.nature.com/articles/s41377-024-01403-7

    Xiao, Y.et al.Multi-junction cascaded vertical-cavity surface-emitting laser with a high power conversion efficiency of 74%.Light: Science & Applications13, 60 (2024). URL https://www.nature.com/articles/s41377-024-01403-7

  41. [41]

    & Javaloyes, J

    Hurtado, A. & Javaloyes, J. Controllable spiking patterns in long-wavelength ver- tical cavity surface emitting lasers for neuromorphic photonics systems.Applied Physics Letters107(2015)

  42. [42]

    & Barland, S

    Turconi, M., Garbin, B., Feyereisen, M., Giudici, M. & Barland, S. Control of excitable pulses in an injection-locked semiconductor laser.Physical Review E 88, 022923 (2013). URL https://link.aps.org/doi/10.1103/PhysRevE.88.022923

  43. [43]

    & Hurtado, A

    Robertson, J., Hejda, M., Bueno, J. & Hurtado, A. Ultrafast optical integra- tion and pattern classification for neuromorphic photonics based on spiking vcsel neurons.Scientific reports10, 6098 (2020)

  44. [44]

    A., Alfaro-Bittner, K., Clerc, M

    Pammi, V. A., Alfaro-Bittner, K., Clerc, M. G. & Barbay, S. Photonic Computing With Single and Coupled Spiking Micropillar Lasers.IEEE Journal of Selected Topics in Quantum Electronics26, 1–7 (2020). URL https://ieeexplore.ieee.org/ document/8765387/

  45. [45]

    & Sciamanna, M

    Vatin, J., Rontani, D. & Sciamanna, M. Experimental reservoir computing using VCSEL polarization dynamics.Optics Express27, 18579 (2019). URL https: //opg.optica.org/abstract.cfm?URI=oe-27-13-18579

  46. [46]

    & Hurtado, A

    Bueno, J., Robertson, J., Hejda, M. & Hurtado, A. Comprehensive performance analysis of a vcsel-based photonic reservoir computer.IEEE Photonics Technology Letters33, 920–923 (2021)

  47. [47]

    URL https://iopscience.iop.org/article/10.1088/2515-7647/abf6bd

    Porte, X.et al.A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser.Journal of Physics: Photonics3, 024017 (2021). URL https://iopscience.iop.org/article/10.1088/2515-7647/abf6bd

  48. [48]

    & Hurtado, A

    Owen-Newns, D., Robertson, J., Hejda, M. & Hurtado, A. Ghz rate neuromorphic photonic spiking neural network with a single vertical-cavity surface-emitting laser (vcsel).IEEE Journal of Selected Topics in Quantum Electronics29, 1–10 (2023)

  49. [49]

    Fisher, R. A. The use of multiple measurements in taxonomic problems.Annals of Eugenics7, 179–188 (1936). URL https://onlinelibrary.wiley.com/doi/10.1111/ j.1469-1809.1936.tb02137.x

  50. [50]

    Baker, J.et al.Vcsel quick fabrication for assessment of large diameter epitaxial wafers.IEEE Photonics Journal14, 1–10 (2022). 18