Abstract
Integrated photonics, because of its intrinsic high speed, large bandwidth and unlimited parallelism, is critical in the drive to ease the increasing data traffic. Its technological enabler is high-precision lithography, which allows for the fabrication of high-resolution photonic structures. Here, in complete contrast to the state of the art, where photonic functions are predefined by lithographically modulating the real index, we report a lithography-free paradigm for an integrated photonic processor, targeting dynamic control of spatial-temporal modulations of the imaginary index on an active semiconductor platform, without the need for lithography. We demonstrate an imaginary-index-driven methodology to tailor optical-gain distributions to rationally execute prescribed optical responses and configure desired photonic functionality to route and switch optical signals. Leveraging its real-time reconfigurability, we realize photonic neural networks with extraordinary flexibility, performing in situ training of vowel recognition with high accuracy. The programmability and multifunctionality intrinsically arising from the lithography-free characteristics can lead to a new paradigm for integrated photonic signal processing to conduct and reconfigure complex computation algorithms, accelerating the information-processing speed to achieve long-term performance requirements.
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Data availability
Data that support the findings of this study are available at https://doi.org/10.6084/m9.figshare.22320649.v1.
Code availability
The computer codes that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
We acknowledge support from the Defense Advanced Research Projects Agency (DARPA) Young Faculty Program (W911NF-21-1-0340), Army Research Office (ARO; W911NF-21-1-0148) and National Science Foundation (NSF; ECCS-2023780). This work was carried out in part at the Singh Center for Nanotechnology, which is supported by the NSF National Nanotechnology Coordinated Infrastructure Program under grant no. NNCI-1542153.
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T.W. and L.F. conceived the project. T.W. and M.M. developed the algorithms and performed simulations. T.W. fabricated the samples and conducted optical measurements. L.F. guided the research. All authors contributed to discussions and paper preparation.
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Extended data
Extended Data Fig. 1 Illustration of the online algorithm.
a, The illustration of the geometry related to input port i and output port j. b, The spatial function f(r). The isovalue contours are the ellipses with 2 focal points at the point sources of the original (red circle) and adjoint field (blue circle). The contours become denser in the place far from the line connecting 2 ports. The white dashed ellipse shows the range \(R(r) \le R_0 = \frac{5}{4}\lambda _{eff}\), which is used for simulations in Supplementary Video 2.
Extended Data Fig. 2 Transmission measurements.
a, Dual-pump optical setup. The 1064 nm pump laser (green trace) is split into two paths for the patterned pumping and the microlaser excitation. The signal around 1500 nm (red trace) is collected by the infrared camera. VA: variable attenuator, OBJ: objective lens, DM: dichroic mirror, PH: pinhole, FM: flip mirror, BPF: band pass filter. b, Target pumping pattern and the pattern generated in experiment. The light spot on the top of the experimental pattern is the zero-order beam from SLM, which does not affect the performance as it is far away from the center. c, Spectrum collected at an input port. d, Spectrum collected at one output port with (red) and without (black) microring lasers excited. e-h Images with different excitation channels. The red and white boxes mark the position of individual microring laser and the output grating. The yellow box indicates the whole imaginary-index-driven area.
Extended Data Fig. 3 Flow chart of the in-situ training.
The initial pattern can be an arbitrary connection between the inputs and the outputs. In each epoch, the inputs and outputs related to all the samples in the dataset are measured. The pumping pattern is updated based on the measurements in the epoch until the accuracy reaches the target.
Supplementary information
Supplementary Information
Supplementary Figs. 1–7 and Table 1.
Supplementary Video 1
Offline algorithm. The left column shows the simulated electric field amplitude with different input channels excited (marked as red). The central column shows the evolution of power transmission. The right column is the target matrix and the evolution of the spatial imaginary index, the target and the simulated transmission matrix (from top to bottom).
Supplementary Video 2
Online algorithm. The same results in Supplementary Video 1 are reproduced using the online algorithm. Although the convergent speed is slightly slower than the offline algorithm, which uses precise gradients, the final performance is also excellent.
Supplementary Video 3
Measurements in one training epoch. Input and output power for 128 vowel data are recorded in one training epoch (m = 30) for vowel recognition. The input signals are encoded by the light power in eight channels on the left, and the four outputs on the right correspond to the four vowel classes. Although the contrast in the outputs is not high for the one-layer network, the integrated powers at output ports yield a nearly perfect recognition accuracy.
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Wu, T., Menarini, M., Gao, Z. et al. Lithography-free reconfigurable integrated photonic processor. Nat. Photon. 17, 710–716 (2023). https://doi.org/10.1038/s41566-023-01205-0
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DOI: https://doi.org/10.1038/s41566-023-01205-0
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