A primary motivation for analog integrated circuit (IC) design automation is the inefficiency of manual design in meeting increasingly stringent specifications, which often involve over 10 objectives. Recent advances in reinforcement learning (RL) emerge as a promising method, yet gaps remain when considering full design specifications, especially under process-voltage-temperature (PVT) variations. Excessive objectives lead to diminished reward signals, while varying PVT conditions result in conflicting gradients, both of which result in inefficient exploration. To address these, we propose a priority-based graph-enhanced RL framework. Specifically, using fuzzy logic converts quantitative rewards into qualitative priority signals, mitigating reward deterioration and enhancing exploration via entropy regularization. Furthermore, a graph-based representation compresses high-dimensional objective spaces under PVT variations into low-dimensional manifolds, enabling dynamic resource allocation to variation-sensitive regions and resolving gradient conflicts. Empirical results on various real-world analog ICs demonstrate that our method significantly outperforms existing RL algorithms, achieving superior solution quality and reducing simulation overhead.

As the design specifications for circuits become increasingly stringent, the demand for automation and intelligence in the sizing of analog integrated circuits (ICs) continues to grow. Algorithms are required to complete circuit optimization tasks under more comprehensive specifications. In this paper, we propose a transferable pole-zero-based transient assertion (PZTA) system. It minimizes the number of high-time-cost transient (Tran) simulations by asserting the stability of the circuit. Furthermore, we utilize a relational graph convolutional network (RGCN) as a function approximator within a reinforcement learning (RL) framework to extract more topological information from the circuits. Experimental results based on the open-source testing suite AnalogGym demonstrate that compared to RGC-NRL before and after embedding the PZTA system, the number of Tran simulations was reduced by 75.68%. Additionally, the assertion accuracy of the model achieved 99.57%.

Direct Volume Rendering (DVR) plays an important role in scientific data visualization. To generate photo-realistic DVR results, the physical light transport throughout the volume is simulated by applying the Monte Carlo-based volumetric path tracing (VPT) approach. For real-time applications, due to the time constraint for rendering each frame, only a limited number of samples shall be taken for the computation per pixel. This can result in a significant amount of noise in the rendering results. This paper describes our optimized VPT sampling algorithm and a novel denoising technique to generate consistently high-quality realistic DVR results in real time. We develop a new shading model that can reduce estimation variance to enhance the quality of DVR results. Additionally, a hybrid acceleration structure is created by integrating both octree and macrocell to improve sampling efficiency. This allows the acquisition of sufficiently more shading samples while maintaining the desired interactive frame rate. To further eliminate remaining noise and improve temporal stability of DVR results, we develop a novel spatiotemporal denoising framework. Our denoiser decouples the estimated radiance into high-detail low-noise and low-detail high-noise components. Different denoising algorithms are separately applied to these components to reduce noise without introducing blurring artifacts. Our DVR system can consistently offer high rendering quality and good temporal stability across DVR result frames in real time. During fast user interactions and with rapid alterations of the illumination condition, our rendering method can still provide good visual comfort and representation accuracy without visible latency.

A high dynamic range (HDR) image is commonly used to reveal stereo illumination, which is crucial for generating high-quality realistic rendering effects. Compared to the high-cost HDR imaging technique, low dynamic range (LDR) imaging provides a low-cost alternative and is preferable for interactive graphics applications. However, the limited LDR pixel bit depth significantly bothers accurate illumination estimation using LDR images. The conflict between the realism and promptness of illumination estimation for realistic rendering is yet to be resolved. In this paper, an efficient method that accurately infers illuminations of real-world scenes using LDR panoramic images is proposed. It estimates multiple lighting parameters, including locations, types and intensities of light sources. In our approach, a new algorithm that extracts illuminant characteristics during the exposure attenuation process is developed to locate light sources and outline their boundaries. To better predict realistic illuminations, a new deep learning model is designed to efficiently parse complex LDR panoramas and classify detected light sources. Finally, realistic illumination intensities are calculated by recovering the inverse camera response function and extending the dynamic range of pixel values based on previously estimated parameters of light sources. The reconstructed radiance map can be used to compute high-quality image-based lighting of virtual models. Experimental results demonstrate that the proposed method is capable of efficiently and accurately computing comprehensive illuminations using LDR images. Our method can be used to produce better realistic rendering results than existing approaches.