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Recursive Inverse Design Enables Hyper-spectral Photonic Integrated Circuits

Hao He, Zengji Tu, Yuanlei Wang, Hongyan Zhao, Chuangxin Feng, Yongzhuo Zhou, Yujun Chen, Ruoao Yang, Lei Zhang, Jianjun Wu, Qi-Fan Yang, Lin Chang

Abstract

Spectrum manipulation is central to photonic systems, where advanced computing and sensing applications often demand highly complex spectral responses to achieve high throughput. Conventional methods for enhancing spectral complexity typically rely on cascading discrete photonic components, resulting in a complexity that scales only linearly with the number of components. Here, we introduce hyper-spectral photonic integrated circuits (HS-PICs), in which spectral complexity scales exponentially with the number of components. This is achieved through recursive inverse design - a system-level inverse design strategy that exploits intricate inter-component interactions as design freedoms, thereby substantially expanding the design space for spectral engineering. Using this approach, we demonstrate that even a single waveguide structure can resolve spectra with sub-picometer resolution, surpassing the performance of current state-of-the-art spectrometers. This performance bridges optical and microwave frequencies in spectral analysis, enabling simultaneous monitoring of optical and radio signals within a single device. Our work establishes a transformative framework for next-generation computing and sensing technologies.

Recursive Inverse Design Enables Hyper-spectral Photonic Integrated Circuits

Abstract

Spectrum manipulation is central to photonic systems, where advanced computing and sensing applications often demand highly complex spectral responses to achieve high throughput. Conventional methods for enhancing spectral complexity typically rely on cascading discrete photonic components, resulting in a complexity that scales only linearly with the number of components. Here, we introduce hyper-spectral photonic integrated circuits (HS-PICs), in which spectral complexity scales exponentially with the number of components. This is achieved through recursive inverse design - a system-level inverse design strategy that exploits intricate inter-component interactions as design freedoms, thereby substantially expanding the design space for spectral engineering. Using this approach, we demonstrate that even a single waveguide structure can resolve spectra with sub-picometer resolution, surpassing the performance of current state-of-the-art spectrometers. This performance bridges optical and microwave frequencies in spectral analysis, enabling simultaneous monitoring of optical and radio signals within a single device. Our work establishes a transformative framework for next-generation computing and sensing technologies.
Paper Structure (9 equations, 4 figures)

This paper contains 9 equations, 4 figures.

Figures (4)

  • Figure 1: Concept and Principle of Hyper-spectral Circuit. ( A) Structure of conventional optical finite impulse response (FIR) filter consists of multi-stage Mach-Zehnder interferometers. The response obtained by the multiplication of individual filters. ( B) The Structure of HS-PIC consists of arranged waveguides connected by routing node, the response is obtained by solving the recursive function $H(N)$. ( C) Schematic of the recursive inverse design. The input is the target spectrum complexity $C$. Three steps sequentially determine the node number (left), geometry (bottom), and layout (right). ( D) Simulated complexity of conventional PIC and HS-PIC with the same waveguide connection distribution. The red dashed line is the linear and exponential fits. ( E) The spectrum shaping behavior of the HS-PIC under combined targets.
  • Figure 2: Recursive Inverse Design of a Straight Waveguide with Complex Response. ( A-E) RID process with different scale complexity targets. From left to right describe the complexity input, recursive optimization, design parameters output, waveguide geometry and its response. Line colors indicate the design wavelengths.(A) One stage result is the router’s response, where dashed lines denote the insertion loss of the router, solid lines represent its reflectivity. Stage number surges from 3 to 16 with the complexity of (B) 10 nm, (C) 1 nm, (D) 0.1 nm and (E) 0.01 nm. ( F) The decomposition process of ideal chain-structured circuits, the complexity decreases as the order increases. ( G) Simulated relation of waveguide loss with respect to stage and router reflectance.
  • Figure 3: Setup and Testing of the Ultra-resolution Spectrometer. ( A) Testing setup of the spectrometer (left), ( B) Optical charged couple device (CCD) image of the fabricated chip (upper), and fake color scanning electron microscope (SEM) of the routers at different operation bands (lower). ( C) The working principle of the spectrometer where phase shifters modulate the relative phase of optical pathways. ( D) Sample channels of the Spectrometer, dashed lines are randomly selected channels and the solid lines are two closest channels, showing a significantly different response. Reconstruction result of ( E) Single-peak laser ( F) dual-peak laser, and ( G) wide-band laser. The noise and performance are marked by SNR and $\varepsilon$.
  • Figure 4: Bridging the Gap Between Optical and Microwave Spectrum Analysis. ( A) Experimental setup of the HS-PIC microwave analysis system (left) and its reconstruction steps (right). The pink and blue signals correspond to the microwave and optical signals, respectively. ( B-D) Comparison between the detection system and electrical spectrum analyzer (ESA) for (i) multi-tone, (ii) QPSK, and (iii) FMCW signal recovery. (B) Results measured by ESA. (C) Results obtained from the detection system. (D) Intermediate optical-domain recovery states for different carrier frequencies. ( E) A collection of recent works on spectrum analysis shows a gap between optical and microwave detection.