Key Takeaways & Executive Findings
- •• Programmable photonic chips based on ultrafast laser-induced phase change are fabricated for photonic computing. • Photonic convolutional neural networks are built on the integrated chip to implement machine learning and achieve image recognition. • Transient laser-induced phase change dynamics of Sb film are revealed at atomic scale, with phase change time measured. • This work enables nonvolatile, low-power programmable photonic chips for AI applications.
Abstract
Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence. Due to the advantages in computing speed, integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm. Programmable photonic chips are vital for achieving practical applications of photonic computing. Herein, a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing. Through designing the ultrafast laser pulses, the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase, resulting in a large contrast in refractive index and extinction coefficient. As a consequence, the light transmission of waveguides can be switched between write and erase states. To determine the phase change time, the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale, and the time-resolved transient reflectivity is measured. Based on the integrated photonic chip, photonic convolutional neural networks are built to implement machine learning algorithm, and images recognition task is achieved. This work paves a route for fabricating programmable photonic chips by designed ultrafast laser, which will facilitate the application of photonic computing in artificial intelligence.
1. Introduction
With the rapid increase in dataset size and computational cost, computing hardware with high calculation speed is strongly desired for the development of machine learning and artificial intelligence [1–7]. Several technologies have been proposed for the next-generation computing accelerator, such as quantum computing [8, 9], neuromorphic computing [10–13], and photonic computing [14–18]. Integrated neuromorphic photonic networks have emerged as a promising hardware accelerator for complex matrix–vector multiplication and convolution operation, which are the fundamental operation in artificial neural networks [19–21]. Compared with electronic devices, integrated photonic devices show advantages in computing speed, throughput, bandwidth density, and power efficiency [22–24]. As the fabrication of silicon photonic integrated circuits (PICs) is compatible with CMOS technology, silicon PICs show a prospective application to establish compact computing units in neural photonic networks [25]. Several silicon PICs architectures have been proposed to realize integrated optical neural networks, such as cascades of multiple Mach–Zehnder interferometers [26, 27], micro-ring resonator-based wavelength division multiplexing [28], waveguide mode converter-based convolution [29], and diffractive neural networks [30].
To realize optical computing with PICs, programmable photonic components are essential building blocks [26, 31]. Electric signals have been used to program the PICs based on thermo-optic effect and free carrier dispersion effects. In general, PICs units are integrated with micro-heaters, which are controlled by electric circuits [14, 30]. By switching the electric current, the temperature of PICs units can be controlled. Owing to thermos-optic effect, the refractive index of silicon waveguide is programmed by turning the temperature [32, 33]. As the thermal conductivity of silicon is poor, the switching speed is limited, and requires large power consumption. Free carrier dispersion effect is another method for programming PICs. By modulating the voltage on waveguide, free carrier concentration (electrons and holes) can be controlled, which can change the refractive index of silicon [34–36]. Both thermo-optic and free carrier dispersion effects are volatile and require supplemental power source to maintain the programmed state, which limit their application in integrated compact devices. Phase change materials (PCMs) have recently emerged as a leading candidate to build nonvolatile programmable PICs without power source [22, 29]. PCMs can be switched between two solid phases, the amorphous and crystalline phases, which show a difference in the complex refractive index. Compared with thermo-optic and free carrier dispersion effects, PCMs show larger change in refractive index and faster switching speed, which have attracted the attention in the fabrication of programmable PICs. For example, by incorporating PCMs with silicon PICs, Majumdar et al. have achieved bro
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Jiawang Xie, Jianfeng Yan, Haoze Han, Yuzhi Zhao, Ma Luo, Jiaqun Li, Heng Guo, Ming Qiao (2025). Photonic Chip Based on Ultrafast Laser-Induced Reversible Phase Change for Convolutional Neural Network. Nano-Micro Letters. https://doi.org/10.1007/s40820-025-01693-5
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Frequently Asked Questions
What is the main contribution of this paper?
The paper demonstrates a programmable photonic chip based on ultrafast laser-induced reversible phase change in Sb films, enabling photonic convolutional neural networks for image recognition.
How does the photonic chip achieve programmability?
The chip uses ultrafast laser pulses to reversibly switch Sb film between crystalline and amorphous phases, changing the refractive index and extinction coefficient, thus modulating light transmission in waveguides.
What are the advantages of using phase change materials in photonic computing?
Phase change materials offer nonvolatile programming, large refractive index contrast, and fast switching speeds compared to thermo-optic and free carrier dispersion effects, enabling low-power and compact devices.
What applications can this technology enable?
This technology can accelerate machine learning and artificial intelligence tasks by performing convolution operations in the optical domain, potentially leading to faster and more energy-efficient computing hardware.
How was the phase change time measured?
The transient laser-induced phase change dynamics of Sb film were revealed at atomic scale, and time-resolved transient reflectivity measurements were used to determine the phase change time.
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