Contrasting the relative performance of RF photonic transversal signal processors based on microcombs using discrete components versus integrated devices

David J. Moss

REVIEW 3 major objections 5 minor 190 references

Contrasting the relative performance of RF photonic transversal signal processors based on microcombs using discrete components versus integrated devices

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Discrete microcomb-based RF processors achieve lower error than integrated ones, and the integrated shortfall comes mainly from having too few taps.

desk verdict Plausible qualitative benchmark, but the headline claim that tap number is the primary limiter for integrated processors is not actually demonstrated by the shown comparisons. read the letter →

arxiv 2502.01641 v1 pith:RAANC472 submitted 2025-01-19 physics.optics

David J. Moss This is my paper · ORCID

classification physics.optics

keywords RFphotonicsopticalmicrocombstransversalsignalprocessorsmicrowavephotonicprocessingintegratedaccuracyRMSEcomparisontapcount

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that, although integrated microcomb-based RF photonic transversal signal processors win on size, weight, and power, they lose to discrete-component versions on processing accuracy, and that the loss is driven mainly by their small tap counts rather than by the imperfections of their individual components. It compares one 80-tap discrete processor with two integrated processors, one at 8 taps and one at 20 taps, on three benchmark functions: first-order differentiation, integration, and the Hilbert transform. In every case the discrete processor has the lowest root-mean-square error, and the 20-tap integrated processor beats the 8-tap one, which shows tap count is the decisive variable. The paper also argues that once component errors are included, adding taps stops helping beyond a point because errors accumulate, and that the integration function is the most demanding of the three. A fair reader would care because the result says which architecture is accurate today and where integrated devices must improve to close the gap.

What carries the argument

The argument rests on the transversal-filter transfer function $H(\omega) = \sum_{n=0}^{M-1} a_n e^{-j\omega n \Delta T}$, which converts each processing function into a set of tap weights $a_n$ on equally spaced wavelength channels from a microcomb, that is, a chip-scale source of many evenly spaced wavelengths. To compare architectures, the paper holds the comb spacing and the delay $\Delta T = 33.4$ ps fixed and injects four component-error parameters into this transfer function: optical signal-to-noise ratio of the comb, modulator chirp $\alpha$, delay-element error, and random tap coefficient error (RTCE). The same error model applied to all three processors isolates the influence of tap number $M$ from the influence of component quality, and RMSE against the ideal output scores the result.

What would settle it

Measure, or simulate with the paper's own error model, the root-mean-square error of the same integrated processor at 8 taps and at 20 taps for differentiation, integration, and Hilbert transform: the paper predicts a clear drop in RMSE for all three functions, so seeing the RMSE stay flat or rise when the tap count increases from 8 to 20 would contradict the claim that limited tap number is the primary accuracy bottleneck.

Watch

Extended reading notes

Core claim

The central claim is a quantitative accuracy ranking: for first-order differentiation, integration, and Hilbert transform, a discrete microcomb-based transversal processor with 80 taps reaches lower RMSE than either an 8-tap or a 20-tap integrated processor. When component errors are removed, discrete and integrated processors with the same tap count have identical RMSE, but with realistic errors the RMSE curves stop decreasing monotonically with tap number, because delay and shaping errors pile up as taps grow. The paper therefore concludes that the primary factor degrading accuracy in current integrated processors is their limited tap count, whereas residual error in discrete processors is mainly due to imperfect experimental components; it further notes that extra errors from cooperative multi-channel operation, left out of the model, would only worsen the integrated processors' standing.

Load-bearing premise

The comparison assumes the error values chosen for each component (20 dB comb optical signal-to-noise ratio, modulator chirp 0.1 for the discrete processor versus 0.8 for the integrated ones, delay errors 4% versus 3%, and tap-weight errors 5% versus 9%) fairly represent real state-of-the-art parts, and that these errors enter the transfer function the way the paper assumes; if either assumption is off, the accuracy ranking and the tap-number conclusion could shift.

Editorial extensions

If this is right

Reaching an RMSE of about 0.05 for differentiation, integration, and Hilbert transform needs roughly 20, 20, and 80 taps respectively, so today's 8- and 12-tap integrated processors cannot match the 80-tap discrete processor on these tasks.
Once component errors are included, RMSE stops falling monotonically as tap number rises, so each architecture has an optimal tap count beyond which extra taps add more error than they remove.
For integrated processors, the highest-leverage improvement is raising the usable tap count while controlling per-tap errors; for discrete processors it is calibrating the spectral shaper and compensating higher-order dispersion in the delay line.
The integration function shows the largest accuracy gap between the architectures, indicating it has the strongest appetite for tap count.
Because the paper excludes extra errors from cooperative operation of many on-chip channels, real integrated processors are likely to land at or below the already-lower modeled accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An implication the author leaves implicit is that closing the integrated-processor gap is mainly a manufacturing and control problem, involving thermal crosstalk, fabrication uniformity, and per-tap calibration, rather than a search for a new operating principle.
The same tap-count-versus-component-error trade-off probably governs other microcomb-driven processors, such as RF channelizers and photonic neural-network accelerators, so their discrete-versus-integrated comparisons may hinge on scaling limits too.
A testable extension is to repeat the RMSE comparison with error parameters measured on the actual devices under test rather than taken from separate literature values, and to include an integrated processor at 40 or 80 taps; the paper's predicted monotone improvement from 8 to 20 taps would show up or fail directly in such measurements.
A fuller system comparison would weight bandwidth, power, and footprint per tap alongside RMSE, since the paper fixes comb spacing and delay across architectures; integrated processors could be preferable on those axes even while losing the accuracy comparison.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a plausible but under-specified simulation benchmark. The comparison question is real and worth airing, but the central attribution—tap number is the primary factor degrading integrated processors—is not supported by the evidence as presented.

What is new: the paper puts discrete and integrated microcomb-based transversal processors side by side in one accuracy framework, and it includes a hypothetical 20-tap integrated processor to probe scalability. It uses realistic component parameters tied to prior experimental work (Refs. 42–47), and the qualitative result—discrete processors can reach more taps and therefore achieve lower error—matches what anyone in the field would expect from the physics. The RMSE plots for differentiation, integration, and Hilbert transform are sensible, and the observation that integration is the most tap-hungry function is useful.

The soft spots are real and central. The error-injection model is not stated. The paper never gives equations for how OSNR, chirp α, delay error tv, and RTCE enter the transfer function of Eq. (3), so the RMSE values in Figs. 2–4 cannot be reproduced. There is no code or data. That alone limits how much weight the quantitative claims can carry.

The bigger problem is the attribution. Table I varies tap count and error parameters together: Processor 1 has M=80, α=0.1, RTCE=5%, tv=4%; Processor 2 has M=8, α=0.8, RTCE=9%, tv=3%. The “tap number only” versus “tap number + experimental errors” curves show the combined effect of all errors, not the isolated effect of tap count. No one-at-a-time ablation separates chirp, RTCE, or delay error. The M=8 integrated processor is not compared against an M=8 discrete processor with the same error parameters, so the claim that tap number dominates is simply not what the data show. Figure 4(b) at M=80 has the integrated processor carrying worse error parameters, so it does not rescue the attribution either.

There is also an internal tension: Section IV argues integrated errors grow superlinearly with tap number, yet Processor 3, the hypothetical M=20 integrated processor, is modeled with the same per-tap error values as the M=8 device. That makes the “increased tap number” scenario optimistic and again conflates tap count with error scaling.

Who this is for: systems engineers choosing between discrete and integrated RF photonic processors will get a qualitative ordering that matches intuition. The quantitative RMSE values should not be cited until the model is specified and the confounds are fixed. The paper deserves a serious referee because the question is legitimate and the problems are addressable: state the error model, release the simulation code, include the existing M=12 integrated processor, and run matched-tap comparisons at M=8 and M=20.

Referee Report

3 major / 5 minor

Summary. The manuscript compares the processing accuracy of microcomb-based RF photonic transversal signal processors assembled from discrete components with fully integrated versions. Using a transversal-filter model (Eq. (3)), the author simulates first-order differentiation, integration, and Hilbert transformation for three representative processors: a discrete 80-tap processor and integrated 8- and 20-tap processors, adopting component error parameters (OSNR, EOM chirp, delay error, RTCE) from the cited experimental literature. The central claims are that current integrated processors have lower accuracy than discrete processors, that the dominant cause is the limited tap number of integrated devices, and that increasing tap count while improving component errors could close the gap. The paper does not provide the error-injection equations, code, or data used to generate the reported RMSE values.

Significance. If the comparison were fully specified and the attribution were supported, the paper would provide a useful systems-level benchmark for a fast-moving device area, quantifying for the first time the accuracy trade-off between discrete and integrated form factors and identifying the tap-count bottleneck. The choice of three elementary signal-processing functions and of real demonstrated tap counts (8 versus 80) is sensible, and the discussion of future scaling in Section IV raises plausible engineering concerns. However, the quantitative RMSE results are not reproducible from the manuscript, and the headline attribution is confounded, so the significance as stated is not yet established.

major comments (3)

[§III, Table I and Figs. 2–3] The statement in Section III that 'the primary factor that contributes to the degradation of accuracy for integrated processors is the limited tap number' is not supported by the evidence shown. Table I changes the tap number and the error parameters simultaneously: Processor 1 has M=80 with α=0.1, RTCE=5%, and tv=4%, while Processor 2 has M=8 with α=0.8, RTCE=9%, and tv=3%. Figs. 2–3 decompose the RMSE into 'limited tap number only' and 'limited tap number + experimental errors', but the second curve is the summed effect of all error sources and cannot separate chirp, RTCE, delay error, or OSNR, nor can it test whether an 8-tap processor with discrete error parameters would match Processor 2. A matched-tap comparison at M=8 with identical error parameters, a one-at-a-time error ablation, or an explicit quantitative attribution of the RMSE difference to each error source is needed.
[§III, Eq. (3) and Table I] The manuscript never states how OSNR, the chirp parameter α, the delay error tv, and the RTCE enter the transfer function of Eq. (3) or the temporal outputs in Figs. 2–4. The RMSE values therefore cannot be reproduced or independently audited, and the sensitivity of the central comparison to the error model cannot be assessed. Please provide the full error-injection model, including any random draws and averaging, and ideally the code or data used for Figs. 2–4; otherwise the quantitative RMSEs should be treated as illustrative rather than as a verified comparison.
[§IV, Fig. 4 and Processor 3] The 'increased tap number' scenario for Processor 3 (M=20) assigns the same per-tap error parameters as Processor 2, although Section IV states that integrated processing errors increase superlinearly with tap number because of fabrication errors, loss, and thermal drift in the added building blocks. This assumption makes the improvement from M=8 to M=20 optimistic and again conflates tap-count effects with error-scaling effects. If the superlinear scaling is part of the argument, it should be modeled explicitly, or the claim should be restricted to the per-tap error model actually used.

minor comments (5)

[Abstract and body text] The abstract and body contain multiple typographical artifacts ('the ir performance', 'u tilize', 't he c', 'del ayed') that should be corrected.
[Eq. (4)] Eq. (4) uses Y1...Yn and y1...yn in the text but Yi and yi in the summation, and the index bound is k rather than n; please make the notation consistent.
[Fig. 1 caption] The caption of Fig. 1(c) repeats 'BPD: balanced photodetector' twice; the duplicate should be deleted.
[Table I] Table I gives OSNR=20 dB for the integrated processors with reference [44], but Ref. [44] is the discrete-processor accuracy study; the provenance of the integrated OSNR value should be clarified.
[§IV, Fig. 4(a)] The statement that DIF, INT, and HT require tap numbers of 20, 20, and 80 to reach RMSE ~0.05 is not derived or connected to a specific error budget; a sentence explaining the criterion would make the claim more concrete.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the RMSE comparisons are computed from Eq. (3) with stated published parameters, and the tap-number attribution rests on controlled internal comparisons rather than on a fitted prediction.

full rationale

The paper does not fit any parameter to the quantity it then claims to predict. The RMSE values in Figs. 3-4 are computed from the transfer function in Eq. (3) using tap weights from prior designs and the component error parameters in Table I, which are stated to be taken from published experimental reports (Refs. [43-47]) rather than inferred from the same outputs. The central attribution, that limited tap number is the primary accuracy-degradation factor for integrated processors, is supported by the controlled internal comparison between Processor 2 (M=8) and Processor 3 (M=20), which share identical error parameters and differ only in tap number, and by the M-sweep in Fig. 4(a) plus the matched-tap M=80 comparison in Fig. 4(b). This is a normal simulation result conditional on the stated assumptions, not a reduction of the output to the input. The self-citations (Refs. [1] and [44]) provide design formulas and parameter provenance rather than load-bearing uniqueness theorems. A legitimate concern is whether the Table I parameters and the unstated error-injection model are representative of state-of-the-art hardware, but that is a correctness and representativeness risk, not circularity.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. Its central comparison rests on a set of hand-chosen component parameters (tap counts, chirp, delay error, RTCE, pulse width) taken from a handful of prior demonstrations, and on an unstated error-injection model. The standard transversal-filter model from Ref [1] is the main mathematical axiom.

free parameters (6)

Tap count M for Processor 2 (integrated) = 8
Selected from Ref [42] to represent state-of-the-art integrated processors; choice of this device over the M=12 device affects the headline comparison.
Tap count M for Processor 3 (integrated) = 20 (hypothetical)
Assumed future integrated processor used to project improvement; not based on a demonstrated device.
Chirp parameter alpha of EOM = 0.1 (discrete), 0.8 (integrated)
Values taken from Refs [45,47]; the difference is a major input to the error model.
Random tap coefficient error (RTCE) = 5% (discrete), 9% (integrated)
Taken from Refs [44,46]; represents spectral shaping accuracy.
Delay element error tv = 4% (discrete), 3% (integrated)
Taken from Refs [43,44]; higher-order dispersion is cited as cause for the discrete value.
Input pulse FWHM = ~0.17 ns
Chosen as the test signal; RMSE results may depend on this choice.

assumptions (4)

domain assumption The transversal filter model in Eqs. (1)-(3) accurately describes the microcomb-based RF photonic processor.
The entire analysis is built on this standard model from Ref [1]; real hardware effects such as crosstalk or nonlinearities are not included.
domain assumption The error-injection model used to compute RMSE (OSNR, chirp, delay errors, RTCE) is correct, although its equations are not stated in the paper.
Introduced implicitly in Section III and Table I; all numerical results depend on this unstated model.
domain assumption The tap coefficients for DIF, INT, and HT are optimally designed as in Ref [1], and RMSE over a Gaussian pulse is a representative accuracy metric.
The paper does not provide the coefficient design or justify the metric beyond a single pulse example.
standard math Fourier analysis and linear time-invariant system theory underpin Eqs. (1)-(3).
Standard mathematical background used without proof.

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Cite this review

Pith. "Pith review of Contrasting the relative performance of RF photonic transversal signal processors based on microcombs using discrete components versus integrated devices." pith.science (2026). https://pith.science/paper/RAANC472

@misc{pith2026250201641,
  author       = {Pith},
  title        = {Pith review of: Contrasting the relative performance of RF photonic transversal signal processors based on microcombs using discrete components versus integrated devices},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RAANC472}},
  note         = {Machine review of arXiv:2502.01641}
}

read the original abstract

RF photonic transversal signal processors, which combine reconfigurable electrical digital signal processing and high-bandwidth photonic processing, provide a powerful solution for achieving adaptive high-speed information processing. Recent progress in optical microcomb technology provides compelling multi-wavelength sources with compact footprint, yielding a variety of microcomb-based RF photonic transversal signal processors implemented by either discrete or integrated components. Although operating based on the same principle, processors in these two forms exhibit distinct performance. This letter presents a comparative investigation into their performance. First, we compare the performance of state-of-the-art processors, focusing on the processing accuracy. Next, we analyze various factors that contribute to the performance differences, including tap number and imperfect response of experimental components. Finally, we discuss the potential for future improvement. These results provide a comprehensive comparison of microcomb based RF photonic transversal signal processors implemented using discrete and integrated components and provide insights for their future development.

Figures

Figures reproduced from arXiv: 2502.01641 by the authors.

**Figure 1.** (a) Schematic illustration of the operation principle of a microcomb-based RF photonic transversal signal processor. (b) Schematic of a microcomb-based RF photonic transversal signal processor implemented by discrete components. (c) Schematic of an on-chip microcombbased RF photonic transversal signal processor implemented by integrated components. EOM: electro-optic modulator. RF: radio frequency. PD: photodetecto… view at source ↗

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Works this paper leans on

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High Conversion Efficiency in Laser Cavity -Soliton Microcombs

Antonio Cutrona, Maxwell Rowley, Debayan Das, Luana Olivieri, Luke Peters, Sai T. Chu, Brent L. Little, Roberto Morandotti, David J. Moss, Juan Sebastian Totero Gongora, Marco Peccianti, Alessia Pasquazi, “High Conversion Efficiency in Laser Cavity -Soliton Microcombs”, Optics...

2022 doi
[120]

Self -emergence of robust solitons in a micro - cavity

M.Rowley, P.Hanzard, A.Cutrona, H.Bao, S.Chu, B.Little, R.Morandotti, D. J. Moss, G. Oppo, J. Gongora, M. Peccianti and A. Pasquazi, “Self -emergence of robust solitons in a micro - cavity”, Nature vol. 608 (7922) 303–309 (2022)

2022
[121]

Nonlocal bonding of a soliton and a blue -detuned state in a microcomb laser

A. Cutrona, M. Rowley, A. Bendahmane, V. Cecconi,L. Peters, L. Olivieri, B. E. Little, S. T. Chu, S. Stivala, R. Morandotti, D. J. Moss, J. S. Totero -Gongora, M. Peccianti, A. Pasquazi, “Nonlocal bonding of a soliton and a blue -detuned state in a microcomb laser”, Nature Com...

2023
[122]

Stability Properties of Laser Cavity-Solitons for Metrological Applications

A. Cutrona, M. Rowley, A. Bendahmane, V. Cecconi,L. Peters, L. Olivieri, B. E. Little, S. T. Chu, S. Stivala, R. Morandotti, D. J. Moss, J. S. Totero -Gongora, M. Peccianti, A. Pasquazi, “Stability Properties of Laser Cavity-Solitons for Metrological Applications”, Applied Phy...

2023 doi
[123]

Quantum optical microcombs

Kues, M. et al. “Quantum optical microcombs”, Nature Photonics 13, (3) 170 -179 (2019). doi:10.1038/s41566-019-0363-0

2019 doi
[124]

Integrated frequency comb source of heralded single photons,

C.Reimer, L. Caspani, M. Clerici, et al., “Integrated frequency comb source of heralded single photons,” Optics Express, vol. 22, no. 6, pp. 6535-6546, 2014

2014
[125]

Cross -polarized photon -pair generation and bi -chromatically pumped optical parametric oscillation on a chip

C.Reimer, et al., “Cross -polarized photon -pair generation and bi -chromatically pumped optical parametric oscillation on a chip”, Nature Communications, vol. 6, Article 8236, 2015. DOI: 10.1038/ncomms9236

2015 doi
[127]

Generation of multiphoton entangled quantum states by means of integrated frequency combs,

C. Reimer et al., “Generation of multiphoton entangled quantum states by means of integrated frequency combs,” Science, vol. 351, no. 6278, pp. 1176-1180, 2016

2016
[128]

On-chip generation of high-dimensional entangled quantum states and their coherent control

M. Kues, et al., “On-chip generation of high-dimensional entangled quantum states and their coherent control”, Nature, vol. 546, no. 7660, pp. 622-626, 2017

2017
[129]

Practical system for the generation of pulsed quantum frequency combs,

P. Roztocki et al., “Practical system for the generation of pulsed quantum frequency combs,” Optics Express, vol. 25, no. 16, pp. 18940-18949, 2017

2017
[130]

Induced photon correlations through superposition of two four-wave mixing processes in integrated cavities

Y. Zhang, et al., “Induced photon correlations through superposition of two four-wave mixing processes in integrated cavities”, Laser and Photonics Reviews, vol. 14, no. 7, pp. 2000128,
[131]

High -dimensional one-way quantum processing implemented on d -level cluster states

C. Reimer, et al., “High -dimensional one-way quantum processing implemented on d -level cluster states”, Nature Physics, vol. 15, no.2, pp. 148–153, 2019

2019
[132]

Complex quantum state generation and coherent control based on integrated frequency combs

P.Roztocki et al., “Complex quantum state generation and coherent control based on integrated frequency combs”, Journal of Lightwave Technology 37 (2) 338-347 (2019)

2019
[133]

Generation and Processing of Complex Photon States with Quantum Frequency Combs

S. Sciara et al., “Generation and Processing of Complex Photon States with Quantum Frequency Combs”, IEEE Photonics Technology Letters 31 (23) 1862 -1865 (2019). DOI: 10.1109/LPT.2019.2944564

2019
[134]

Scalable and effective multilevel entangled photon states: A promising tool to boost quantum technologies

Stefania Sciara, Piotr Roztocki, Bennet Fisher, Christian Reimer, Luis Romero Cortez, William J. Munro, David J. Moss, Alfonso C. Cino, Lucia Caspani, Michael Kues, J. Azana, and Roberto Morandotti, “Scalable and effective multilevel entangled photon states: A promising tool t...

2021 doi
[135]

Multifrequency sources of quantum correlated photon pairs on-chip: a path toward integrated Quantum Frequency Combs,

L. Caspani, C. Reimer, M. Kues, et al., “Multifrequency sources of quantum correlated photon pairs on-chip: a path toward integrated Quantum Frequency Combs,” Nanophotonics, vol. 5, no. 2, pp. 351-362, 2016

2016
[136]

Enhanced supercontinuum generated in SiN waveguides coated with GO films

Yuning Zhang, Jiayang Wu, Yang Qu, Yunyi Yang, Linnan Jia, Baohua Jia, and David J. Moss, “Enhanced supercontinuum generated in SiN waveguides coated with GO films”, Advanced Materials Technologies 8 (1) 2201796 (2023). DOI: 10.1002/admt.202201796

2023 doi
[137]

Graphene oxide for nonlinear integrated photonics

Yuning Zhang, Jiayang Wu, Linnan Jia, Yang Qu, Baohua Jia, and David J. Moss, “Graphene oxide for nonlinear integrated photonics”, Laser and Photonics Reviews 17 2200512 (2023). DOI:10.1002/lpor.202200512

2023 doi
[138]

Graphene oxide: new opportunities for electronics, photonics, and optoelectronics

Jiayang Wu, H. Lin, D. J. Moss, T.K. Loh, Baohua Jia, “Graphene oxide: new opportunities for electronics, photonics, and optoelectronics”, Nature Reviews Chemistry 7 (3) 162 –183 (2023). DOI:10.1038/s41570-022-00458-7

2023 doi
[139]

Photo thermal tuning in GO-coated integrated waveguides

Yang Qu, Jiayang Wu, Yuning Zhang, Yunyi Yang, Linnan Jia, Baohua Jia, and David J. Moss, “Photo thermal tuning in GO-coated integrated waveguides”, Micromachines 13 1194 (2022). doi.org/10.3390/mi13081194

2022 doi
[140]

Enhanced self-phase modulation in silicon nitride waveguides integrated with 2D graphene oxide films

Yuning Zhang, Jiayang Wu, Yunyi Yang, Yang Qu, Houssein El Dirani, Romain Crochemore, Corrado Sciancalepore, Pierre Demongodin, Christian Grillet, Christelle Monat, Baohua Jia, and David J. Moss, “Enhanced self-phase modulation in silicon nitride waveguides integrated with 2D ...

2023
[141]

Enhanced spectral broadening of femtosecond optical pulses in silicon nanowires integrated with 2D graphene oxide films

Yuning Zhang, Jiayang Wu, Yunyi Yang, Yang Qu, Linnan Jia, Baohua Jia, and David J. Moss, “Enhanced spectral broadening of femtosecond optical pulses in silicon nanowires integrated with 2D graphene oxide films”, Micromachines 13 756 (2022). DOI:10.3390/mi13050756

2022 doi
[142]

Fabrication Technologies for the On -Chip Integration of 2D Materials

Linnan Jia, Jiayang Wu, Yuning Zhang, Yang Qu, Baohua Jia, Zhigang Chen, and David J. Moss, “Fabrication Technologies for the On -Chip Integration of 2D Materials”, Small: Methods 6, 2101435 (2022). DOI:10.1002/smtd.202101435

2022 doi
[143]

Design and optimization of four -wave mixing in microring resonators integrated with 2D graphene oxide films

Yuning Zhang, Jiayang Wu, Yang Qu, Linnan Jia, Baohua Jia, and David J. Moss, “Design and optimization of four -wave mixing in microring resonators integrated with 2D graphene oxide films”, Journal of Lightwave Technology 39 (20) 6553 -6562 (2021). DOI:10.1109/JLT.2021.3101292...

2021
[144]

Optimizing the Kerr nonlinear optical performance of silicon waveguides integrated with 2D graphene oxide films

Yuning Zhang, Jiayang Wu, Yang Qu, Linnan Jia, Baohua Jia, and David J. Moss, “Optimizing the Kerr nonlinear optical performance of silicon waveguides integrated with 2D graphene oxide films”, Journal of Lightwave Technology 39 (14) 4671 -4683 (2021). DOI: 10.1109/JLT.2021.3069733

2021
[145]

Analysis of four-wave mixing in silicon nitride waveguides integrated with 2D layered graphene oxide films

Yang Qu, Jiayang Wu, Yuning Zhang, Yao Liang, Baohua Jia, and David J. Moss, “Analysis of four-wave mixing in silicon nitride waveguides integrated with 2D layered graphene oxide films”, Journal of Lightwave Technology 39 (9) 2902 -2910 (2021). DOI: 10.1109/JLT.2021.3059721

2021
[146]

Graphene oxide: versatile films for flat optics to nonlinear photonic chips

Jiayang Wu, Linnan Jia, Yuning Zhang, Yang Qu, Baohua Jia, and David J. Moss,“ Graphene oxide: versatile films for flat optics to nonlinear photonic chips”, Advanced Materials 33 (3) 2006415, pp.1-29 (2021). DOI:10.1002/adma.202006415

2021 doi
[147]

Graphene oxide for enhanced optical nonlinear performance in CMOS compatible integrated devices

Y. Qu, J. Wu, Y. Zhang, L. Jia, Y. Yang, X. Xu, S. T. Chu, B. E. Little, R. Morandotti, B. Jia, and D. J. Moss, “Graphene oxide for enhanced optical nonlinear performance in CMOS compatible integrated devices”, Paper No. 11688 -30, PW21O -OE109-36, 2D Photon ic Materials and D...

2021 doi
[148]

Enhanced nonlinear four -wave mixing in silicon n itride waveguides integrated with 2D layered graphene oxide films

Yang Qu, Jiayang Wu, Yunyi Yang, Yuning Zhang, Yao Liang, Houssein El Dirani, Romain Crochemore, Pierre Demongodin, Corrado Sciancalepore, Christian Grillet, Christelle Monat, Baohua Jia, and David J. Moss, “Enhanced nonlinear four -wave mixing in silicon n itride waveguides i...

2020 arXiv
[149]

Enhanced Kerr nonlinearity and nonlinear figure of merit in silicon nanowires integrated with 2D graphene oxide films

Yuning Zhang, Yang Qu, Jiayang Wu, Linnan Jia, Yunyi Yang, Xingyuan Xu, Baohua Jia, and David J. Moss, “Enhanced Kerr nonlinearity and nonlinear figure of merit in silicon nanowires integrated with 2D graphene oxide films”, ACS Applied Materials and Interf aces vol. 12 (29) 33...

2020 doi
[150]

Enhanced nonlinear four-wave mixing in microring resonators integrated with layered graphene oxide fi lms

Jiayang Wu, Yunyi Yang, Yang Qu, Yuning Zhang, Linnan Jia, Xingyuan Xu, Sai T. Chu, Brent E. Little, Roberto Morandotti, Baohua Jia,* and David J. Moss*, “Enhanced nonlinear four-wave mixing in microring resonators integrated with layered graphene oxide fi lms”, Small vol. 16 ...

2020 doi
[151]

Graphene oxide waveguide polarizers and polarization selective micro-ring resonators

Jiayang Wu, Yunyi Yang, Yang Qu, Xingyuan Xu, Yao Liang, Sai T. Chu, Brent E. Little, Roberto Morandotti, Baohua Jia, and David J. Moss, “Graphene oxide waveguide polarizers and polarization selective micro-ring resonators”, Paper 11282-29, SPIE Photonics West, San Francisco, ...

2020 doi
[152]

Graphene oxide waveguide polarizers and polarization selective micro -ring resonators

Jiayang Wu, Yunyi Yang, Yang Qu, Xingyuan Xu, Yao Liang, Sai T. Chu, Brent E. Little, Roberto Morandotti, Baohua Jia, and David J. Moss, “Graphene oxide waveguide polarizers and polarization selective micro -ring resonators”, Laser and Photonics Reviews vol . 13 (9) 1900056 (2...

2019 doi
[153]

Enhanced four -wave mixing in graphene oxide coated waveguides

Yunyi Yang, Jiayang Wu, Xingyuan Xu, Sai T. Chu, Brent E. Little, Roberto Morandotti, Baohua Jia, and David J. Moss, “Enhanced four -wave mixing in graphene oxide coated waveguides”, Applied Physics Letters Photonics vol. 3 120803 (2018). doi: 10.1063/1.5045509

2018 doi
[154]

Third-order optical nonlinearities of 2D materials at telecommunications wavelengths

Linnan Jia, Yang Qu, Jiayang Wu, Yuning Zhang, Yunyi Yang, Baohua Jia, and David J. Moss, “Third-order optical nonlinearities of 2D materials at telecommunications wavelengths”, Micromachines (MDPI), 14, 307 (2023). https://doi.org/10.3390/mi14020307

2023 doi
[155]

Sagnac interference in integrated photonics

Hamed Arianfard, Saulius Juodkazis, David J. Moss, and Jiayang Wu, “Sagnac interference in integrated photonics”, Applied Physics Reviews vol. 10 (1) 011309 (2023). doi: 10.1063/5.0123236. (2023)

2023 doi
[156]

Optical analogs of Rabi splitting in integrated waveguide -coupled resonators

Hamed Arianfard, Jiayang Wu, Saulius Juodkazis, and David J. Moss, “Optical analogs of Rabi splitting in integrated waveguide -coupled resonators”, Advanced Physics Research 2 (2023). DOI: 10.1002/apxr.202200123

2023 doi
[157]

Spectral shaping based on optical waveguides with advanced Sagnac loop reflectors

Hamed Arianfard, Jiayang Wu, Saulius Juodkazis, and David J. Moss, “Spectral shaping based on optical waveguides with advanced Sagnac loop reflectors”, Paper No. PW22O-OE201-20, SPIE-Opto, Integrated Optics: Devices, Materials, and Technologies XXVI, SPIE Photonics West, San F...

2022 doi
[158]

Spectral Shaping Based on Integrated Coupled Sagnac Loop Reflectors Formed by a Self-Coupled Wire Waveguide

Hamed Arianfard, Jiayang Wu, Saulius Juodkazis, David J. Moss, “Spectral Shaping Based on Integrated Coupled Sagnac Loop Reflectors Formed by a Self-Coupled Wire Waveguide”, IEEE Photonics Technology Letters vol. 33 (13) 680 -683 (2021). DOI:10.1109/LPT.2021.3088089

2021
[159]

Three Waveguide Coupled Sagnac Loop Reflectors for Advanced Spectral Engineering

Hamed Arianfard, Jiayang Wu, Saulius Juodkazis and David J. Moss, “Three Waveguide Coupled Sagnac Loop Reflectors for Advanced Spectral Engineering”, Journal of Lightwave Technology vol. 39 (11) 3478-3487 (2021). DOI: 10.1109/JLT.2021.3066256

2021
[160]

Advanced Multi - Functional Integrated Photonic Filters based on Coupled Sagnac Loop Reflectors

Hamed Arianfard, Jiayang Wu, Saulius Juodkazis and David J. Moss, “Advanced Multi - Functional Integrated Photonic Filters based on Coupled Sagnac Loop Reflectors”, Journal of Lightwave Technology vol. 39, Issue: 5, 1400-1408 (2021). DOI:10.1109/JLT.2020.3037559

2021
[161]

Advanced multi - functional integrated photonic filters based on coupled Sagnac loop reflectors

Hamed Arianfard, Jiayang Wu, Saulius Juodkazis and David J. Moss, “Advanced multi - functional integrated photonic filters based on coupled Sagnac loop reflectors”, Paper 11691- 4, PW21O-OE203-44, Silicon Photonics XVI, SPIE Photonics West, San Francisco CA March 6-11 (2021). ...

2021 doi
[162]

Advanced photonic filters via cascaded Sagnac loop reflector resonators in silicon -on-insulator integrated nanowires

Jiayang Wu, Tania Moein, Xingyuan Xu, and David J. Moss, “Advanced photonic filters via cascaded Sagnac loop reflector resonators in silicon -on-insulator integrated nanowires”, Applied Physics Letters Photonics vol. 3 046102 (2018). DOI:/10.1063/1.5025833

2018 doi
[163]

Micro-ring resonator quality factor enhancement via an integrated Fabry -Perot cavity

Jiayang Wu, Tania Moein, Xingyuan Xu, Guanghui Ren, Arnan Mitchell, and David J. Moss, “Micro-ring resonator quality factor enhancement via an integrated Fabry -Perot cavity”, Applied Physics Letters Photonics vol. 2 056103 (2017). doi: 10.1063/1.4981392

2017 doi
[164]

BiOBr nanoflakes with strong nonlinear optical properties towards hybrid integrated photonic devices

Linnan Jia, Dandan Cui, Jiayang Wu, Haifeng Feng, Tieshan Yang, Yunyi Yang, Yi Du, Weichang Hao, Baohua Jia, David J. Moss, “BiOBr nanoflakes with strong nonlinear optical properties towards hybrid integrated photonic devices”, Applied Physics Letters Photonics vol. 4 090802 (...

2019 doi
[165]

Large Third-Order Optical Kerr Nonlinearity in Nanometer-Thick PdSe2 2D Dichalcogenide Films: Implications for Nonlinear Photonic Devices

Linnan Jia, Jiayang Wu, Yunyi Yang, Yi Du, Baohua Jia, David J. Moss, “Large Third-Order Optical Kerr Nonlinearity in Nanometer-Thick PdSe2 2D Dichalcogenide Films: Implications for Nonlinear Photonic Devices”, ACS Applied Nano Materials vol. 3 (7) 6876–6883 (2020). DOI:10.102...

2020 doi
[166]

Full -band-structure calculation of first -, second-, and third-harmonic optical response coefficients of ZnSe, ZnTe, and CdTe

E.D Ghahramani, DJ Moss, JE Sipe, “Full -band-structure calculation of first -, second-, and third-harmonic optical response coefficients of ZnSe, ZnTe, and CdTe”, Physical Review B 43 (12), 9700 (1991)

1991
[167]

Efficient coupling to chalcogenide glass photonic crystal waveguides via silica optical fiber nanowires

C Grillet, C Smith, D Freeman, S Madden, B Luther-Davies, EC Magi, ... “Efficient coupling to chalcogenide glass photonic crystal waveguides via silica optical fiber nanowires”, Optics Express vol. 14 (3), 1070-1078 (2006)

2006
[168]

High -Q cavities in photosensitive photonic crystals

S Tomljenovic-Hanic, MJ Steel, CM de Sterke, DJ Moss, “High -Q cavities in photosensitive photonic crystals” Optics Letters vol. 32 (5), 542-544 (2007)

2007
[169]

On -Chip ultra -fast 1st and 2nd order CMOS compatible all -optical integration

M Ferrera et al., “On -Chip ultra -fast 1st and 2nd order CMOS compatible all -optical integration”, Optics Express vol. 19 (23), 23153-23161 (2011)

2011
[170]

Error free all optical wavelength conversion in highly nonlinear As -Se chalcogenide glass fiber

VG Ta’eed et al., “Error free all optical wavelength conversion in highly nonlinear As -Se chalcogenide glass fiber”, Optics Express vol. 14 (22), 10371-10376 (2006)

2006
[171]

2R optical regeneration: an all -optical solution for BER improvement

M Rochette, L Fu, V Ta'eed, DJ Moss, BJ Eggleton, “2R optical regeneration: an all -optical solution for BER improvement”, IEEE Journal of Selected Topics in Quantum Electronics vol. 12 (4), 736-744 (2006)

2006
[172]

Silicon -chip-based real -time dispersion monitoring for 640 Gbit/s DPSK signals

TD Vo, et al., “Silicon -chip-based real -time dispersion monitoring for 640 Gbit/s DPSK signals”, Journal of Lightwave Technology vol. 29 (12), 1790-1796 (2011)

2011
[173]

Modeling of complex integrated photonic resonators using scattering matrix method

Di Jin, Jiayang Wu, Sian Ren, Junkai Hu, Duan Huang, and David J. Moss, “Modeling of complex integrated photonic resonators using scattering matrix method”, Photonics, Vol. 11, 1107 (2024). https://doi.org/10.3390/photonics11121107

2024 doi
[174]

Enhancing laser temperature stability by passive self- injection locking to a micro-ring resonator

Yonghang Sun, James Salamy, Caitlin E. Murry, Brent E. Little, Sai T. Chu, Roberto Morandotti, Arnan Mitchell, David J. Moss, Bill Corcoran, “Enhancing laser temperature stability by passive self- injection locking to a micro-ring resonator”, Optics Express Vol. 32 (13) 23841-...

2024 doi
[175]

Raman scattering and supercontinuum generation in high-index doped silica chip waveguides

C. Khallouf, V. T. Hoang, G. Fanjoux, B. Little, S. T. Chu, D. J. Moss, R. Morandotti, J. M. Dudley, B. Wetzel, and T. Sylvestre, “Raman scattering and supercontinuum generation in high-index doped silica chip waveguides”, Nonlinear Optics and its Applications, edited by John ...

2024 doi
[176]

Performance analysis of microwave photonic spectral filters based on optical microcombs

Yang Li, Yang Sun, Jiayang Wu, Guanghui Ren, Roberto Morandotti, Xingyuan Xu, Mengxi Tan, Arnan Mitchell, and David J. Moss, “Performance analysis of microwave photonic spectral filters based on optical microcombs”, Advanced Physics Research, Vol. 3 (9) 2400084 (2024). DOI:10....

2024 doi
[177]

Silicon photonic waveguide and microring resonator polarizers incorporating 2D graphene oxide films

Di Jin, Jiayang Wu, Junkai Hu, Wenbo Liu, Yuning Zhang, Yunyi Yang, Linnan Jia, Duan Huang, Baohua Jia, and David J. Moss, “Silicon photonic waveguide and microring resonator polarizers incorporating 2D graphene oxide films”, Applied Physics Letters, Vol. 125, 053101 (2024). d...

2024 doi
[178]

Novel functionality with 2D graphene oxide films integrated on silicon photonic chips

Jiayang Wu, Yuning Zhang, Junkai Hu, Yunyi Yang, Di Jin, Wenbo Liu, Duan Huang, Baohua Jia, David J. Moss, “Novel functionality with 2D graphene oxide films integrated on silicon photonic chips”, Advanced Materials, Vol. 36, 2403659 (2024). DOI: 10.1002/adma.202403659

2024 doi
[179]

Advanced optical polarizers based on 2D materials

Yuning Zhang, Jiayang Wu, Linnan Jia, Di Jin, Baohua Jia, Xiaoyong Hu, David Moss, Qihuang Gong, “Advanced optical polarizers based on 2D materials”, npj Nanophotonics, Vol. 1 (2024). DOI: 10.1038/s44310-024-00028-3

2024 doi
[180]

2D graphene oxide: a versatile thermo-optic material

Junkai Hu, Jiayang Wu, Wenbo Liu, Di Jin, Houssein El Dirani, Sébastien Kerdiles, Corrado Sciancalepore, Pierre Demongodin, Christian Grillet, Christelle Monat, Duan Huang, Baohua Jia, and David J. Moss, “2D graphene oxide: a versatile thermo-optic material”, Advanced Function...

2024 doi
[181]

Thickness and Wavelength Dependent Nonlinear Optical Absorption in 2D Layered MXene Films

Di Jin, Wenbo Liu, Linnan Jia, Junkai Hu, Duan Huang, Jiayang Wu, Baohua Jia, and David J. Moss, “Thickness and Wavelength Dependent Nonlinear Optical Absorption in 2D Layered MXene Films”, Small Science Vol. 4, 2400179 (2024). DOI:10.1002/smsc202400179

2024 doi
[182]

Parametric interaction of laser cavity-solitons with an external CW pump

Andrew Cooper, Luana Olivieri, Antonio Cutrona, Debayan Das, Luke Peters, Sai Tak Chu, Brent Little, Roberto Morandotti, David J Moss, Marco Peccianti, and Alessia Pasquazi, “Parametric interaction of laser cavity-solitons with an external CW pump”, Optics Express Vol. 32 (12)...

2024
[183]

Photonic RF Channelization Based on Microcombs

Weiwei Han, Zhihui Liu, Yifu Xu, Mengxi Tan, Chaoran Huang, Jiayang Wu, Kun Xu, David J. Moss, and Xingyuan Xu, “Photonic RF Channelization Based on Microcombs”, Special Issue on Microcombs IEEE Journal of Selected Topics in Quantum Electronics Vol. 30 (5) 7600417 (2024). DOI:...

2024
[184]

Feedback control in micro-comb-based microwave photonic transversal filter systems

Y. Li, Y. Sun, J. Wu, G. Ren, X. Xu, M. Tan, S. Chu, B. Little, R. Morandotti, A. Mitchell, and D. J. Moss, “Feedback control in micro-comb-based microwave photonic transversal filter systems”, IEEE Journal of Selected Topics in Quantum Electronics Vol. 30 (5) 2900117 (2024). ...

2024
[185]

Dual- polarization RF Channelizer Based on Microcombs

Weiwei Han, Zhihui Liu, Yifu Xu, Mengxi Tan, Yuhua Li, Xiaotian Zhu, Yanni Ou, Feifei Yin, Roberto Morandotti, Brent E. Little, Sai Tak Chu, Xingyuan Xu, David J. Moss, and Kun Xu, “Dual- polarization RF Channelizer Based on Microcombs”, Optics Express Vol. 32, No. 7, 11281-11...

2024 doi
[186]

Mode-locked laser with multiple timescales in a microresonator-based nested cavity

Aadhi A. Rahim, Imtiaz Alamgir, Luigi Di Lauro, Bennet Fischer, Nicolas Perron, Pavel Dmitriev, Celine Mazoukh, Piotr Roztocki, Cristina Rimoldi, Mario Chemnitz, Armaghan Eshaghi, Evgeny A. Viktorov, Anton V. Kovalev, Brent E. Little, Sai T. Chu, David J. Moss, and Roberto Mor...

2024 doi
[187]

Genetic algorithm-enhanced microcomb state generation

C. Mazoukh, L. Di Lauro, I. Alamgir1 B. Fischer, A. Aadhi, A. Eshaghi, B. E. Little, S. T. Chu, D. J. Moss, and R. Morandotti, “Genetic algorithm-enhanced microcomb state generation”, Special Issue Microresontaor Frequency Combs - New Horizons, Nature Communications Physics, V...

2024 doi
[188]

Graphene oxide for enhanced nonlinear optics in integrated photonic chips

Y. Zhang, J. Wu, Y. Yang,Y. Qu, L. Jia, C. Grillet, C. Monat, B. Jia, and D.J. Moss, “Graphene oxide for enhanced nonlinear optics in integrated photonic chips”, Paper 12888-16, Conference OE109, 2D Photonic Materials and Devices VII, Chair(s): Arka Majmdar; Carlos M. Torres J...

2024 doi
[189]

Graphene oxide-based waveguides for enhanced self- phase modulation

Zhang Y, Wu J, Qu Y, Jia L, Jia B, D.J. Moss, “Graphene oxide-based waveguides for enhanced self- phase modulation”, Annals of Mathematics and Physics Vol. 5 (2) 103-106 (2022). DOI:10.17352/amp.000048

2022 doi
[190]

Microcombs for Optical Communications

Bill Corcoran, Arnan Mitchell, Roberto Morandotti, Leif K. Oxenlowe, and David J. Moss, “Microcombs for Optical Communications”, Nature Photonics, Vol. 19 (2025)

2025
[2020]

DOI: 10.1002/lpor.202000128

doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Applications of optical microcombs,

Y. Sun, J. Wu, M. Tan, X. Xu, Y. Li, R. Morandotti, A. Mitchell, and D. J. Moss, “Applications of optical microcombs,” Advances in Optics and Photonics, vol. 15, no. 1, pp. 86-175, 2023

2023

[2] [2]

11 TOPS photonic convolutional accelerator for optical neural networks,

X. Xu, M. Tan, B. Corcoran, J. Wu, A. Boes, T. G. Nguyen, S. T. Chu, B. E. Little, D. G. Hicks, R. Morandotti, A. Mitchell, and D. J. Moss, “11 TOPS photonic convolutional accelerator for optical neural networks,” Nature, vol. 589, no. 7840, pp. 44-51, 2021/01/01, 2021

2021

[3] [3]

Dual-polarization RF Channelizer Based on Microcombs

Weiwei Han, Zhihui Liu, Yifu Xu, Mengxi Tan, Yuhua Li, Xiaotian Zhu, Yanni Ou, Feifei Yin, Roberto Morandotti, Brent E. Little, Sai Tak Chu, Xingyuan Xu, David J. Moss, and Kun Xu, “Dual-polarization RF Channelizer Based on Microcombs”, Journal of Lightwave Technology (2023)

2023

[4] [4]

Photonic signal processor for real-time video image processing at 17 Tb/s based on a Kerr microcomb

Mengxi Tan, Xingyuan Xu, Andreas Boes, Bill Corcoran, Thach G. Nguyen, Sai T. Chu, Brent E. Little, Roberto Morandotti, Jiayang Wu, Arnan Mitchell, and David J. Moss, “Photonic signal processor for real-time video image processing at 17 Tb/s based on a Kerr microcomb”, Nature Communications Engineering 2 (2023)

2023

[5] [5]

Performance analysis of microwave photonic spectral filters based on optical microcombs

Yang Li, Yang Sun, Jiayang Wu, Guanghui Ren, Roberto Morandotti, Xingyuan Xu, Mengxi Tan, Arnan Mitchell, and David J. Moss, “Performance analysis of microwave photonic spectral filters based on optical microcombs”, Optics and Lasers in Engineering (2023)

2023

[6] [6]

Optimizing the performance of microcomb based microwave photonic transversal signal processors

Yang Sun, Jiayang Wu, Yang Li, Xingyuan Xu, Guanghui Ren, Mengxi Tan, Sai Tak Chu, Brent E. Little, Roberto Morandotti, Arnan Mitchell, and David J. Moss, “Optimizing the performance of microcomb based microwave photonic transversal signal processors”, Journal of Lightwave Technology 41 (2023)

2023

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Quantifying the Accuracy of Microcomb- based Photonic RF Transversal Signal Processors

Yang Sun, Jiayang Wu, Yang Li, Mengxi Tan, Xingyuan Xu, Sai Chu, Brent Little, Roberto Morandotti, Arnan Mitchell, and David J. Moss, “Quantifying the Accuracy of Microcomb- based Photonic RF Transversal Signal Processors”, IEEE Journal of Selected Topics in Quantum Electronics vol. 29, no. 6, pp. 1-17, Art no. 7500317 (2023)

2023

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Photonic multiplexing techniques for neuromorphic computing

Yunping Bai, Xingyuan Xu, Mengxi Tan, Yang Sun, Yang Li, Jiayang Wu, Roberto Morandotti, Arnan Mitchell, Kun Xu, and David J. Moss, “Photonic multiplexing techniques for neuromorphic computing”, Nanophotonics 12 (5): 795–817 (2023)

2023