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Abstract Optical phase modulators are critical components in integrated photonics, but conventional designs suffer from a trade-off between modulation efficiency and optical loss. Two-dimensional materials like graphene offer strong electro-optic effects, yet their high optical absorption at telecom wavelengths leads to significant insertion losses. Although monolayer transition metal dichalcogenides (TMDs) provide exceptional telecom-band transparency for low-loss electro-refractive response, their practical implementation in phase modulators requires top electrodes to enable vertical electric field tuning, which typically introduces parasitic absorption. Here, we address this challenge by developing hybrid tungsten oxyselenide/graphene (TOS/Gr) electrodes that minimize optical loss while enabling efficient phase modulation in TMD-based devices. The UV-ozone-converted TOS (from WSe₂) acts as a heavy p-type dopant for graphene, making the graphene transparent in the NIR region while enhancing its conductivity. Our complete device integrates a hybrid TOS/graphene transparent electrode with a hexagonal boron nitride dielectric spacer and monolayer WS₂electro-optic material on a SiN microring platform. This achieves a high modulation efficiency of 0.202 V·cm while maintaining an exceptionally low extinction ratio change of just 0.08 dB, demonstrating superior performance compared to modulators employing conventional electrodes. Our breakthrough in near-lossless phase modulation opens new possibilities for energy-efficient optical communications, photonic computing, and fault-tolerant quantum networks. 

Double-electrode gas metal arc welding (DE-GMAW) modifies GMAW by adding a second electrode to bypass a portion of the current flowing from the wire. This reduces the current to, and the heat input on, the workpiece. Successful bypassing depends on the relative position of the bypass electrode to the continuously varying wire tip. To ensure proper operation, we propose robotizing the system using a follower robot to carry and adaptively adjust the bypass electrode. The primary information for monitoring this process is the arc image, which directly shows desired and undesired modes. However, developing a robust algorithm for processing the complex arc image is time-consuming and challenging. Employing a deep learning approach requires labeling numerous arc images for the corresponding DE-GMAW modes, which is not practically feasible. To introduce alternative labels, we analyze arc phenomena in various DE-GMAW modes and correlate them with distinct arc systems having varying voltages. These voltages serve as automatically derived labels to train the deep-learning network. The results demonstrated reliable process monitoring. 

Gas metal arc welding (GMAW) is the most robotized arc welding process and most widely used process for wire arc additive manufacturing (WAAM). Double-electrode GMAW (DE-GMAW) is its novel modification achieved by adding a second/bypass electrode. It provides the capability to freely adjust the base metal current (heat input) without changing the wire current (mass input). However, it must be robotized in order to be adaptive to manufacturing variations and constraints. Due to the complexity of the process, we propose learning from human welders through their attempts to adjust the bypass electrode in collaboration with the robotized GMAW. To generalize human success using a follower robot/ surrogate, the distance between the wire and bypass electrode is proposed as the process state to quantify human observation and operation. As such, Inertial Measurement Unit (IMU) sensors are integrated to track the wire and bypass electrode operated by both the lead robot and the human welder. To interpret human adjustments per arc observation, a Convolutional Neural Network (CNN) is employed to process arc images and calculate the distance. The automatically obtained distance labels from IMU signals are used to train the CNN; however, they are noisy and inaccurate. Thus, the CNN is finely tuned through transfer learning using manually labeled distances. With accurately automatically calculated distances from the finely tuned CNN for all the data, the demonstrations from human welders are analyzed. The generalized knowledge is implemented by a robotic surrogate, substituting for the human welder to fully automate the DE-GMAW. Experiments demonstrated the superior performance of the fully robotized, and adaptively controlled, DE-GMAW process.