Tin halide perovskites have served as channel materials for p-type transistors owing to their low hole effective mass and suitable hole density. However, they suffer from uncontrolled film crystallization, leading to excessive tin vacancies and self-p-doping. In this study, we report a facile way to grow three-dimensional (3D) tin halide perovskite films by thermal evaporation on a dangling-bond-free hexagonal boron nitride (hBN) surface. The hBN, transferred onto SiO2 as a gate dielectric/channel interlayer, offers a hydrophobic surface that promotes the crystallization of CsSnI3 films by reducing the nucleation site density, increasing the nuclei size, and promoting the formation of uniformly oriented enlarged grains. CsSnI3 films grown on hBN exhibit reduced pinholes and grain boundaries, reducing the concentration of tin vacancies. Thin-film transistors using these films demonstrate accelerated charge transport with large current modulation without any additives. The proposed strategy can facilitate the engineering of defect-free perovskite channel layers for integrated perovskite electronics.

With the demand for high-performance and miniaturized semiconductor devices continuously rising, the development of innovative tunneling transistors via efficient stacking methods using two-dimensional (2D) building blocks has paramount importance in the electronic industry. Hence, 2D semiconductors with atomically thin geometries hold significant promise for advancements in electronics. In this study, we introduced tunneling memtransistors with a thin-film heterostructure composed of 2D semiconducting MoS₂ and WSe₂. Devices with the dual function of tuning and memory operation were realized by the gate-regulated modulation of the barrier height at the heterojunction and manipulation of intrinsic defects within the exfoliated nanoflakes using solution processes. Further, our investigation revealed extensive edge defects and four distinct defect types, namely monoselenium vacancies, diselenium vacancies, tungsten vacancies, and tungsten adatoms, in the interior of electrochemically exfoliated WSe₂ nanoflakes. Additionally, we constructed complementary metal-oxide semiconductor-based logic-in-memory devices with a small static power in the range of picowatts using the developed tunneling memtransistors, demonstrating a promising approach for next-generation low-power nanoelectronics.

Generating optimal trajectories in dynamic environments is crucial for advanced autonomous driving. Analyzing multi-level processes individually can obscure the interdependencies between levels, resulting in suboptimal trajectories. Furthermore, short-term planning often fails to anticipate dynamic road conditions, thereby limiting hazard identification. This leads to steering or speed control errors due to high computational demands, which ultimately compromise smooth driving. To address these challenges, this study proposes a robust framework that integrates multi-level modules to generate optimal trajectories and to execute predicted state-based long-term planning. In particular, we employ Hierarchical Reinforcement Learning (HRL): the upper level makes high-level driving decisions, and the generated trajectory serves as an objective function for the lower-level motion planner, which is executed by a low-level controller. Additionally, the framework incorporates dynamic state prediction of surrounding vehicles, enabling long-term planning based on predicted state vectors. To evaluate the proposed framework, various scenarios were simulated using the CARLA autonomous driving simulator. Results show that the framework significantly outperforms baseline models in trajectory smoothness, computational efficiency, hazard avoidance, adaptability, and learning performance. These improvements demonstrate its effectiveness in dynamic multi-lane environments for autonomous driving.

This study develops a comprehensive artificial neural network (ANN) model for predicting the mechanical properties of carbon–manganese cast steel, specifically, the yield strength (YS), tensile strength (TS), elongation (El), and reduction of area (RA), based on the chemical composition (16 alloying elements) and heat treatment parameters. The neural network model, employing a 20-44-44-4 architecture and trained on 400 samples from an industrial dataset of 500 samples, achieved 90% of test predictions within a 5% deviation from actual values, with mean prediction errors of 3.45% for YS and 4.9% for %EL. A user-friendly graphical interface was developed to make these predictive capabilities accessible, without requiring programming expertise. Sensitivity analyses revealed that increasing the copper content from 0.05% to 0.2% enhanced the yield strength from 320 to 360 MPa while reducing the ductility, whereas niobium functioned as an effective grain refiner, improving both the strength and ductility. The combined effects of carbon and manganese demonstrated complex synergistic behavior, with the yield strength varying between 280 and 460 MPa and the tensile strength ranging from 460 to 740 MPa across the composition space. Optimal strength–ductility balance was achieved at moderate compositions of 1.0–1.2 wt% Mn and 0.20–0.24 wt% C. The model provides an efficient alternative to costly experimental trials for optimizing C-Mn steels, with prediction errors consistently below 6% compared with 8–20% for traditional empirical methods. This approach establishes quantitative guidelines for designing complex multi-element alloys with targeted mechanical properties, representing a significant advancement in computational material engineering for industrial applications.

Tin halide perovskites have served as channel materials for p-type transistors owing to their low hole effective mass and suitable hole density. However, they suffer from uncontrolled film crystallization, leading to excessive tin vacancies and self-p-doping. In this study, we report a facile way to grow three-dimensional (3D) tin halide perovskite films by thermal evaporation on a dangling-bond-free hexagonal boron nitride (hBN) surface. The hBN, transferred onto SiO2 as a gate dielectric/channel interlayer, offers a hydrophobic surface that promotes the crystallization of CsSnI3 films by reducing the nucleation site density, increasing the nuclei size, and promoting the formation of uniformly oriented enlarged grains. CsSnI3 films grown on hBN exhibit reduced pinholes and grain boundaries, reducing the concentration of tin vacancies. Thin-film transistors using these films demonstrate accelerated charge transport with large current modulation without any additives. The proposed strategy can facilitate the engineering of defect-free perovskite channel layers for integrated perovskite electronics.

With the demand for high-performance and miniaturized semiconductor devices continuously rising, the development of innovative tunneling transistors via efficient stacking methods using two-dimensional (2D) building blocks has paramount importance in the electronic industry. Hence, 2D semiconductors with atomically thin geometries hold significant promise for advancements in electronics. In this study, we introduced tunneling memtransistors with a thin-film heterostructure composed of 2D semiconducting MoS₂ and WSe₂. Devices with the dual function of tuning and memory operation were realized by the gate-regulated modulation of the barrier height at the heterojunction and manipulation of intrinsic defects within the exfoliated nanoflakes using solution processes. Further, our investigation revealed extensive edge defects and four distinct defect types, namely monoselenium vacancies, diselenium vacancies, tungsten vacancies, and tungsten adatoms, in the interior of electrochemically exfoliated WSe₂ nanoflakes. Additionally, we constructed complementary metal-oxide semiconductor-based logic-in-memory devices with a small static power in the range of picowatts using the developed tunneling memtransistors, demonstrating a promising approach for next-generation low-power nanoelectronics.