This paper presents a systematic study on developing multi-template machine learning (ML) surrogate models and applying them to the inverse design of transformers (XFMRs) in radio-frequency integrated circuits (RFICs). Our study starts with benchmarking four widely used ML architectures, including MLP-, CNN-, UNet-, and GT-based models, using the same datasets across different XFMR topologies. To improve modeling accuracy beyond these baselines, we then propose a new frequency-domain self-transfer learning technique that exploits correlations between adjacent frequency bands, leading to $\sim 30 \%-50 \%$ accuracy improvement in the S-parameters prediction. Building on these models, we further develop an inverse design framework based on the covariance matrix adaptation evolutionary strategy (CMA-ES) algorithm. This framework is validated using multiple impedance-matching tasks, all demonstrating fast convergence and trustworthy performance. These results advance the goal of AI-assisted “specs-to-GDS” automation for RFICs and provide RFIC designers with actionable tools for integrating AI into their workflows.

High-quality fundus images are critical for clinical diagnosis, yet real-world acquisition challenges often introduce multi-component degradations. Current deep learning methods typically address single degradations, lacking a unified handling of complex scenarios. In this paper, we propose the Multi-degradation Fundus Image Restoration Network (MFR-Net), an all-in-one restoration framework integrating frequency-aware prompt learning. MFR-Net comprehensively extracts the frequency domain features of different degradation components, and injects them into the backbone network through designed prompt generation and interaction modules. Furthermore, to enhance the model's domain generalization capability, the unsupervised domain adaptation is incorporated into a more reliable perceptual and image quality-oriented space for domain alignment. Extensive experimental results demonstrate that the proposed method outperforms several state-of-the-art models in the restoration of degraded retinal images, especially in the restoration of complex degradations in real images, where the quantitative indicators have been improved by up to 5.42% compared with SOTA algorithms.

This paper presents how we leverage AI-human collaboration to develop an end-to-end, automated design flow for digitally controlled oscillators (DCOs), a key radio-frequency (RF) integrated circuits (ICs) building block that dominates phase noise and jitter performance of RF systems. Specifically, we decompose the DCO design process into two steps and use AI to enhance productivity and optimize performance within each step. Additionally, we demonstrate how AI can assist RF IC designers in creating unconventional circuit components to tackle challenging design specifications. Overall, the proposed flow is capable of synthesizing the DCO design including the schematic and layout in 80 seconds after one-time training, and is frequency agile between 1 and 20 GHz. Moreover, it can select the most robust design under process variations when multiple design parameters meet target specifications under the nominal condition. The proposed automated DCO design flow is demonstrated using two silicon prototypes implemented in the GlobalFoundries $22-\mathrm{nm}$ CMOS SOI process. In the measurements, they achieve $\gt 192.4-\mathrm{dBc} / \mathrm{Hz}$ figure-of-merit (FoM) and $\lt 1.5-\mathrm{kHz}$ frequency resolution at 7.1 to 8.6 GHz and 3.8 to 4.6 GHz, outperforming existing manual designs at similar frequencies.

The front-end synthesis of analog circuits has been a long-standing challenge since the advent of integrated circuits. Many methods, ranging from conventional optimization-based techniques to emerging learning-based approaches, have been extensively explored to address this challenge. Yet, these methods are data-driven and often suffer from low design efficiency, due to their heavy reliance on time-consuming circuit simulators, which are frequently used in the synthesis loop for real-time evaluation of the evolving circuit design. In addition, benchmarking these methods is also largely unachievable due to their exclusive use of commercial semiconductor technology for evaluation. This “Late Breaking Results” introduces Opera, an open and efficient platform for the data-driven synthesis of analog circuits. Specifically, Opera develops efficient surrogate models for various circuits and integrates them into open-source OpenAI Gym-like environments to enable efficient synthesis. Case studies on exemplary circuits show that this platform can accelerate the conventional data-driven synthesis flow by up to $40 \times$. It also enables the benchmarking of various synthesis methods with standardized environments built upon an open-source semiconductor process.

This paper presents a design methodology that can effectively extend the bandwidth of a cascode common-source with inductive degeneration low noise amplifier (LNA), which is one of the most popular LNA topologies in the millimeter-wave bands. Specifically, this methodology addresses how to broaden the input matching bandwidth by realizing dual-resonant${S}$11, and how to extend the gain bandwidth by synthesizing a transformer-based second-order bandpass output network. As a proof of concept, a 27–46 GHz LNA is implemented in the GlobalFoundries 45-nm CMOS SOI process, achieving 25.5–50 GHz 3-dB gain bandwidth, 27–46 GHz return loss bandwidth, 21.2 dB peak gain, 2.4 dB minimum noise figure, and −9.5 dBm peak IIP 3, under 25.5 mW DC power consumption. Consistent performance is measured across multiple samples, demonstrating the robustness of the presented design methodology.