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1. Non-volatile materials for programmable photonics

   https://doi.org/10.1063/5.0165309  

   Fang, Zhuoran; Chen, Rui; Tossoun, Bassem; Cheung, Stanley; Liang, Di; Majumdar, Arka (October 2023, APL Materials)

   Programmable photonics play a crucial role in many emerging applications, from optical accelerators for machine learning to quantum information technologies. Conventionally, photonic systems are tuned by mechanisms such as the thermo-optic effect, free carrier dispersion, the electro-optic effect, or micro-mechanical movement. Although these physical effects allow either fast (>100 GHz) or large contrast (>60 dB) switching, their high static power consumption is not optimal for programmability, which requires only infrequent switching and has a long static time. Non-volatile materials, such as phase-change materials, ferroelectrics, vanadium dioxide, and memristive metal oxide materials, can offer an ideal solution thanks to their reversible switching and non-volatile behavior, enabling a truly “set-and-forget” programmable unit with no static power consumption. In recent years, we have indeed witnessed the fast adoption of non-volatile materials in programmable photonic systems, including photonic integrated circuits and free-space meta-optics. Here, we review the recent progress in the field of programmable photonics, based on non-volatile materials. We first discuss the material’s properties, operating mechanisms, and then their potential applications in programmable photonics. Finally, we provide an outlook for future research directions. The review serves as a reference for choosing the ideal material system to realize non-volatile operation for various photonic applications. 

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2. Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications

   https://doi.org/10.1002/adma.202205047  

   Liu, Hefei; Qin, Yuan; Chen, Hung‐Yu; Wu, Jiangbin; Ma, Jiahui; Du, Zhonghao; Wang, Nan; Zou, Jingyi; Lin, Sen; Zhang, Xu; et al (March 2023, Advanced Materials)

   Abstract Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed. 

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3. The MiniHawk-VTOL: Design, Modeling, and Experiments of a Rapidly-prototyped Tiltrotor UAV

   Carlson, S.; Papachristos, C. (July 2021, 2021 International Conference on Unmanned Aircraft Systems)

   null (Ed.)

   This work addresses the rapidly-prototyped design of a small Tricopter/Fixed-Wing Vertical Take-Off and Landing UAS with solar-recharge-capability, capable of repeatedly landing, recharging, and taking off, without need for physical intervention or externally placed maintenance devices or platforms. The design uses Fused Deposition Modeling 3D printing to rapidly prototype and fabricate the majority of the aircraft structures and parts. Provisions are made for carrying high-level single board computing solutions, or other similar payloads. Details are provided for mechanisms, aerodynamic geometry, solar cell integration and manufacturability. The design is analyzed to estimate inertial moments, aerodynamic performance, and static and dynamic stability. Simulation models for the Gazebo and RealFlight environments are provided, targeting Software-In-The-Loop architectures that run the ArduPilot and PX4 flight stacks. A flight testing methodology is developed, and results are presented with multiple prototype vehicles constructed. We finally contribute all production definitions, files, and models as open-access resources, with the goal of supporting and promoting migratory/swarming behavior and autonomy research. 

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4. Information dynamics in the network of cyber-physical systems

   Wang, Yan (May 2020, Proceedings of 13th International Symposium on Tools and Methods of Competitive Engineering (TMCE2020))

   null (Ed.)

   Cyber-physical systems (CPS) are physical devices with highly integrated functionalities of sensing, computing, communication, and control. The levels of intelligence and functions that CPS can perform heavily rely on their intense collaboration and information sharing through networks. In this paper, the information propagation within CPS networks is studied. Information dynamics models are proposed to characterize the evolution of information processing capabilities of CPS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing functions of CPS nodes are captured as the probabilities of correct predictions, whereas the communication functions are represented as the probabilities of mutual influences between nodes. In the proposed copula dynamics model, the information dependency among individuals is represented with joint prediction probabilities and estimated from copulas of extremal probabilities. In the proposed functional interdependency model, the correlations between prediction capabilities are captured with their functional relationships. A data-driven approach is taken to train the parameters of the information dynamics models with data from simulations. The information dynamics models are demonstrated with a simulator of CPS networks. 

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5. Fast and Energy‐Efficient Non‐Volatile III‐V‐on‐Silicon Photonic Phase Shifter Based on Memristors

   https://doi.org/10.1002/adom.202301178  

   Fang, Zhuoran; Tossoun, Bassem; Descos, Antoine; Liang, Di; Huang, Xue; Kurczveil, Geza; Majumdar, Arka; Beausoleil, Raymond G. (October 2023, Advanced Optical Materials)

   Abstract Silicon photonics has evolved from lab research to commercial products in the past decade as it plays an increasingly crucial role in data communication for next‐generation data centers and high‐performance computing. Recently, programmable silicon photonics has also found new applications in quantum and classical information processing. A key component of programmable silicon photonic integrated circuits (PICs) is the phase shifter, traditionally realized via thermo‐optic or free‐carrier effects that are weak, volatile, and power hungry. A non‐volatile phase shifter can circumvent these limitations by requiring zero power to maintain the switched phases. Previously non‐volatile phase modulation is achieved via phase‐change or ferroelectric materials, but the switching energy remains high (pico to nano joules) and the speed is slow (micro to milliseconds). Here, a non‐volatile III‐V‐on‐silicon photonic phase shifter based on a HfO₂memristor with sub‐pJ switching energy (≈400 fJ), representing over an order of magnitude improvement in energy efficiency compared to the state of the art, is reported. The non‐volatile phase shifter can be switched reversibly using a single 100 ns pulse and exhibits excellent endurance over 800 cycles. This technology can enable future energy‐efficient programmable PICs for data centers, optical neural networks, and quantum information processing. 

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