In recent years, electronic design automation methodologies based on hierarchical multilevel bottom-up (BU) design approaches are emerging and successfully applied for RF system design. In this article, we propose a design automation methodology for the synthesis of millimeter-wave (mm-wave) systems via BU approaches, including sizing and layout generation. First, uniformly sampled passive and active component libraries with prepared layouts and S-parameter files are constructed during the offline preparation stage. Second, the BU sizing from the device level to the system level has been demonstrated via multiobjective optimization algorithms, while an improved Euclidean mapping strategy is proposed to efficiently search over circuit-level Pareto-optimal fronts (POFs) in the system-level optimization. Third, the parameterized DRC/LVS clean layout can be hierarchically generated for the system-level POFs. Compared to flat optimization at the system level, the proposed method greatly reduces the size of the search space with the highest accuracy possible and can be used for the synthesis of complex mm-wave systems. The proposed method achieves a$10\times $runtime speedup in the system-level optimization with better optimization results.

In this article, we propose an efficient surrogate-assisted constrained multiobjective evolutionary algorithm for analog circuit sizing via self-adaptive incremental learning. The proposed approach reduces the total optimization time in four aspects. First, by reusing the previously trained models, the incremental learning technique is introduced to reduce the time complexity of training the Kriging model from$O(n^{3})$to$O(n^{2})$, where$n$is the number of training points. Second, a self-adaptive strategy to control when to update hyperparameters is proposed to further reduce the training time of the Kriging model. Third, our method is driven by prescreening the most promising population instead of internal optimization which saves the prediction time of the Kriging model. Fourth, the maximin distance-based expected improvement matrix criterion is introduced as the acquisition function to formulate multiple objectives into a scalar function, reducing the sorting time to rank population. Experimental results on three real-world circuits demonstrate that compared with the state-of-the-art multiobjective Bayesian optimization, our method achieves a speedup of up to$13\times $in total runtime without surrendering optimization results. To be more specific, our method reduces the training time of the Kriging model by 96%, the prediction time by 99%, and the sorting time to rank population by up to 92%. Compared with NSGA-II, there is up to$6\times $speedup in terms of the total runtime with better results.

In this paper, we propose an efficient Kriging-based constrained multi-objective evolutionary algorithm for analog circuit synthesis via self-adaptive incremental learning. The incremental learning technique is introduced to reduce time complexity of training the Kriging model from$O(n^{3})$, to$O(n^{2})$, where$n$is the number of training points. The proposed approach reduces the total optimization time in three aspects. First, by reusing the previously trained models, a self-adaptive incremental learning strategy is applied to reduce the training time of the Kriging model. Second, we use non-dominated sorting and modified crowding distance to prescreen the most promising one to be simulated, which largely reduce the number of simulations. Third, as there is no internal optimization, the prediction time of the Kriging model is saved. Experimental results on two real-world circuits demonstrate that compared with the state-of-the-art multi-objective Bayesian optimization, our method can reduce the training time of Kriging model by 95% and the prediction time by 99.7% without surrendering optimization results. Compared with NSGA-II and MOEA/D, the proposed method can achieve up to 10X speed up in terms of the total optimization time while achieving better results.

Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.