Optical modulators are indispensable components in optical communication systems and must be designed to minimize insertion loss, reduce driving voltage, and enhance linearity. State-of-the-art silicon modulator technology has limitations in terms of power, performance, and spatial size. The addition of materials such as thin-film lithium niobate (TFLN), silicon–organic hybrids (SOH), and plasma–organic hybrids (POH) has improved the modulation performance in silicon photonics. An evaluation of the differences among these modulators and their respective performance characteristics is conducted.

In this paper, the fabrication and the corresponding performance characteristics of resonant cavity micro-light-emitting diodes (RC-μ-LEDs) are examin

Free-space optical transceiver modules require precise lens assembly to achieve efficient laser-to-fiber coupling. However, optical coupling efficiency is highly sensitive to axial spacing, optical component parameters, and alignment conditions, making conventional parameter tuning time-consuming and inefficient. In this study, a simulation-assisted deep reinforcement learning (DRL) framework is proposed for lens assembly optimization in free-space optical transceiver modules. A dual-lens optical coupling system consisting of a laser diode, a collimating lens, a coupling lens, an optical isolator, and a single-mode fiber was established using a ray-tracing optical simulation environment. A deep deterministic policy gradient agent based on an actor–critic architecture was employed to search the continuous assembly parameter space and maximize the optical coupling efficiency. Under the common benchmark, all five methods produced solutions close to the independent reference efficiency. BO and PSO required the fewest evaluations to reach the 0.95η_REF and 0.99η_REF targets, whereas DDPG showed greater run-to-run variability. To further clarify the influence of reward design on optimization behavior, seven reward-weighting conditions spanning efficiency-only to stability-only formulations were investigated to examine the influence of reward composition on the DDPG optimization behavior. The reward-efficiency analysis indicates that an overemphasis on a single objective may lead to ineffective optimization behavior, whereas the equal-weight condition produced a visually more concentrated coupling-efficiency trajectory in the representative run; however, additional repeated-run and tolerance analyses are required to establish improved stability. These results demonstrate the potential of the proposed simulation-based framework for evaluating optimization strategies in optical design and lens assembly, while further experimental and hardware-level validation is required to assess its applicability to practical optical packaging.