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The increasing complexity of integrated circuit design requires customizing Power, Performance, and Area (PPA) metrics according to different application demands. However, most engineers cannot anticipate requirements early in the design process, often discovering mismatches only after synthesis, necessitating iterative optimization or redesign. Some works have shown the promising capabilities of large language models (LLMs) in hardware design generation tasks, but they fail to tackle the PPA trade-off problem. In this work, we propose an LLM-based reinforcement learning framework, PPA-RTL, aiming to introduce LLMs as a cutting-edge automation tool by directly incorporating post-synthesis metrics PPA into the hardware design generation phase. We design PPA metrics as reward feedback to guide the model in producing designs aligned with specific optimization objectives across various scenarios. The experimental results demonstrate that PPA-RTL models, optimized for Power, Performance, Area, or their various combinations, significantly improve in achieving the desired trade-offs, making PPA-RTL applicable to a variety of application scenarios and project constraints. 

Integrated circuit design is a highly complex and time-consuming process. Leveraging large language models (LLMs) for automating hardware design generation is receiving increasing attention. A prominent challenge is that the inherent structure of the text is overlooked during the training process. Existing efforts focus on supervised fine-tuning LLMs to acquire specialized knowledge in hardware design, without considering the conflict between LLMs' linear data processing and the structural nature inherent in hardware design. In this work, we propose a novel LLM-based reinforcement learning (RL) framework that integrates Abstract Syntax Trees (ASTs) and Data Flow Graphs (DFGs). Our approach enhances the accuracy of generated hardware code by capturing the syntactic and semantic structures of hardware designs. Experimental results show that the SFT-RL model integrated with Text, AST, and DFG achieves notable improvements: a 12.57% increase on VerilogEval-Human and a 5.49% increase on VerilogEval-Machine, outperforming GPT-4; a 14.29% improvement on RTLLM, approaching GPT-4. 

In this work, we propose a balanced multicomponent and multilayer neural network (MMNN) structure to accurately and efficiently approximate functions with complex features in terms of both degrees of freedom and computational cost. The main idea is inspired by a multicomponent approach in which each component can be effectively approximated by a single-layer network, combined with a multilayer decomposition strategy to capture the complexity of the target function. Although MMNNs can be viewed as a simple modification of fully connected neural networks (FCNNs) or multilayer perceptrons (MLPs) by introducing balanced multicomponent structures, they achieve a significant reduction in training parameters, a much more efficient training process, and improved accuracy compared to FCNNs or MLPs. Extensive numerical experiments demonstrate the effectiveness of MMNNs in approximating highly oscillatory functions and their ability to automatically adapt to localized features. Our code and implementations are available at GitHub. 

Methods based on upward canopy gap fractions are widely employed to measure in-situ effective LAI (Le) as an alternative to destructive sampling. However, these measurements are limited to point-level and are not practical for scaling up to larger areas. To address the point-to-landscape gap, this study introduces an innovative approach, named NeRF-LAI, for corn and soybean Le estimation that combines gap-fraction theory with the neural radiance field (NeRF) technology, an emerging neural network-based method for implicitly representing 3D scenes using multi-angle 2D images. The trained NeRF-LAI can render downward photorealistic hemispherical depth images from an arbitrary viewpoint in the 3D scene, and then calculate gap fractions to estimate Le. To investigate the intrinsic difference between upward and downward gaps estimations, initial tests on virtual corn fields demonstrated that the downward Le matches well with the upward Le, and the viewpoint height is insensitive to Le estimation for a homogeneous field. Furthermore, we conducted intensive real-world experiments at controlled plots and farmer-managed fields to test the effectiveness and transferability of NeRF-LAI in real-world scenarios, where multi-angle UAV oblique images from different phenological stages were collected for corn and soybeans. Results showed the NeRF-LAI is able to render photorealistic synthetic images with an average peak signal-to-noise ratio (PSNR) of 18.94 for the controlled corn plots and 19.10 for the controlled soybean plots. We further explored three methods to estimate Le from calculated gap fractions: the 57.5° method, the five-ring-based method, and the cell-based method. Among these, the cell-based method achieved the best performance, with the r2 ranging from 0.674 to 0.780 and RRMSE ranging from 1.95 % to 5.58 %. The Le estimates are sensitive to viewpoint height in heterogeneous fields due to the difference in the observable foliage volume, but they exhibit less sensitivity to relatively homogeneous fields. Additionally, the cross-site testing for pixel-level LAI mapping showed the NeRF-LAI significantly outperforms the VI-based models, with a small variation of RMSE (0.71 to 0.95 m2/m2) for spatial resolution from 0.5 m to 2.0 m. This study extends the application of gap fraction-based Le estimation from a discrete point scale to a continuous field scale by leveraging implicit 3D neural representations learned by NeRF. The NeRF-LAI method can map Le from raw multi-angle 2D images without prior information, offering a potential alternative to the traditional in-situ plant canopy analyzer with a more flexible and efficient solution. 

As conventional electronic materials approach their physical limits, the application of ultrafast optical fields to access transient states of matter cap- tures imagination. The inversion symmetry governs the optical parity selection rule, differentiating between accessible and inaccessible states of matter. To circumvent parity-forbidden transitions, the common practice is to break the inversion symmetry by material design or external fields. Here we report how the application of femtosecond ultraviolet pulses can energize a parity-forbidden dark exciton state in black phosphorus while maintaining its intrinsic material symmetry. Unlike its conventional bandgap absorption in visible-to-infrared, femtosecond ultraviolet excitation turns on efficient Coulomb scattering, promoting carrier multiplication and electronic heating to ~3000 K, and consequently populating its parity-forbidden states. Interfero- metric time- and angle-resolved two-photon photoemission spectroscopy reveals dark exciton dynamics of black phosphorus on ~100 fs time scale and its anisotropic wavefunctions in energy-momentum space, illuminating its potential applications in optoelectronics and photochemistry under ultraviolet optical excitation.