Benefiting from high parallelism and low latency, photonic integrated circuits (PICs) constructed from on-chip building blocks with diverse functions have emerged as a promising technology in the realm of optical neural networks (ONN). Tunable components, through the utilization of physical mechanisms such as thermo-optic effect and free-carrier plasma dispersion effect, structural motion like microelectromechanical systems (MEMS), or material properties including liquid crystal and two-dimensional materials, play a pivotal role in enabling reconfigurability within PICs. Among these reconfiguration schemes, chalcogenide phase change materials (PCMs) based photonic devices have attracted extensive attention owing to their high energy efficiency and integration density brought by huge refractive index contrasts and nonvolatility of PCMs. However, this nonvolatile modulation method meets difficulty in scalability since the process flow of integrating PCMs into silicon photonics is insupportable in the foundries. Here, we demonstrated a back-end-of-line (BEOL) integration platform for the monolithic integration of PCMs into silicon photonic devices without modification in standard process design kits (PDK). This is achieved by fabricating a low-loss oxide trench to expose the waveguide core at the functional area from the top dielectric layer, with assistance from a silicon nitride etch stop layer. On this basis, integrated photonic devices with stable switching performance and repeatable multi-bit storage capability have been developed, possessing the potential for crucial blocks of PICs in ONN applications that require infrequent reconfiguration, such as hardware error correction before training and data storage in pre-trained models.
Currently, integrated optoelectronic technology has made significant progress in commercial applications. However, existing technologies are approaching their theoretical limits. How to introduce new materials to achieve novel on-chip optical field control and generate disruptive breakthroughs will be crucial for meeting the future demands of optical computing, optical communication, optical sensing, and other applications. Chalcogenide materials, also known as chalcogenide glass materials, mainly refer to compounds containing sulfur, selenium, tellurium, and other chalcogen elements. They not only possess excellent nonlinear optical properties and excellent micro-nano processing characteristics but also some specific compositions of chalcogenide glass exhibit nonvolatile phase transition characteristics for exploring nonvolatile reconfigurable photon platforms. This paper will mainly introduce some progress in our research on scalable fabrication techniques for integrated photonic devices based on chalcogenide materials.
Intelligent photonics, driven by silicon photonics, is revolutionizing high-speed data processing, low-power computing, and precision sensing. Leveraging these advances, photonic chips are enabling the development of optical neural networks and nonlinear activation mapping, which are crucial for addressing the demands of large generative models. However, traditional on-chip control methods struggle with high power consumption and volatility. To overcome these challenges, phase-change materials (PCMs) offer high optical contrast and non-volatility, enhancing integration density and reducing power usage. This article discusses the performance and reversible control of PCMs and their integration with silicon photonics. By incorporating PCMs into in-memory optical computing chips, we achieved 4-bit storage and over 88% accuracy on the MNIST dataset, marking significant progress in next-generation high-performance computing.
Nonvolatile light-field manipulation via electrically-driven phase transition of chalcogenide phase change materials (PCMs) is regarded as one of the most powerful solutions to low-power-consumption and compact integrated reconfigurable photonics. However, before the breakthrough in large-scale integration approaches linked to wafer foundries, phase-change non-volatile reconfigurable photonics could hardly see their widespread practical applications. Here we demonstrate nonvolatile photonic devices fabricated by back-end-of-line (BOEL) integration of PCMs into the commercial silicon photonics platform. A narrow trench etched into the BOEL dielectric layer exposed the waveguide core and allowed for the direct deposition of various PCM films on the waveguide in the functional areas. Fine-tuning the nonvolatile phase transition of Sb2Se3 via a PIN microheater was verified by realizing the post-fabrication trimming of silicon photonic devices. Our work highlights a reliable platform for large-scale PCM-integrated photonics and validates its precise nonvolatile reconfigurability.
Optical neural networks (ONNs), enabling low latency and high parallel data processing without electromagnetic interference, have become a viable player for fast and energy-efficient processing and calculation to meet the increasing demand for hash rate. Photonic memories employing nonvolatile phase-change materials could achieve zero static power consumption, low thermal cross talk, large-scale, and high-energy-efficient photonic neural networks. Nevertheless, the switching speed and dynamic energy consumption of phase-change material-based photonic memories make them inapplicable for in situ training. Here, by integrating a patch of phase change thin film with a PIN-diode-embedded microring resonator, a bifunctional photonic memory enabling both 5-bit storage and nanoseconds volatile modulation was demonstrated. For the first time, a concept is presented for electrically programmable phase-change material-driven photonic memory integrated with nanosecond modulation to allow fast in situ training and zero static power consumption data processing in ONNs. ONNs with an optical convolution kernel constructed by our photonic memory theoretically achieved an accuracy of predictions higher than 95% when tested by the MNIST handwritten digit database. This provides a feasible solution to constructing large-scale nonvolatile ONNs with high-speed in situ training capability.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.