The emergence of back-side interconnects aims to sustain the continued scaling of semiconductor technology. To extend existing back-end tools, netlist planning has been introduced to transform single-sided netlists into double-sided ones, thereby exploring the potential of utilizing bridging cells for double-sided signal routing. However, the lack of a native double-sided routing approach that fully leverages both front-side and back-side resources hinders the effective handling of complex systematic requirements. In light of this, we propose a native double-sided signal routing approach DSR for the first time, which realizes efficient cross-layer path selection in 3D routing space by unified modeling of front-side and back-side resources. We develop a native double-sided global routing algorithm that jointly considers resource allocation and bridging cell insertion, guided by delay models for performance optimization. Under the guidance of global routing, we further extend the double-sided routing graph and incorporate delay-aware mechanisms to enhance resource allocation and routing quality in detailed routing. Experimental results demonstrate that, compared with existing works, the proposed approach achieves significant improvements in delay and runtime, while maintaining wirelength and eliminating Design Rule Violations (DRVs).

Deep learning has emerged as the predominant technique for predicting routability in Very-Large-Scale-Integrated (VLSI) circuits. However, it often struggles to generalize to various tasks and performs poorly when addressing inherent data imbalance issues in electronic design automation. Overcoming these challenges typically requires retraining or fine-tuning models, which poses significant difficulties for chip engineers who lack resources and expertise in neural network training. In light of this, we propose and address the universal problem of routability prediction for the first time. By framing this issue as a meta-learning scenario, we propose a Few-Shot Learning (FSL)-based approach, URoute, which adapts flexibly to new tasks by utilizing features of the query chip and labeled examples without additional training. To tackle the data imbalance problem, we further propose a meta-learning strategy based on importance sampling to optimize the model training process. To validate the generality and adaptability of URoute, we construct an FSL dataset based on CircuitNet and ISPD2015 datasets. Experimental results demonstrate that URoute exhibits greater robustness and flexibility compared to existing methods when handling unseen routability prediction tasks, achieving competitive results.

The Steiner minimum tree (SMT) serves as an optimal connection model for multiterminal nets in very large scale integration (VLSI). Constructing both rectilinear SMT (RSMT) and octilinear SMT (OSMT) are known to be NP-hard problems. Simultaneously, constructing multiple topologies of SMTs for a given net holds significant importance in alleviating routing constraints such as alleviating congestion and ensuring timing convergence. However, existing efforts predominantly focus on designing specialized methods to construct a specifically structured SMT for a given net, making it challenging to extend to different structures or topologies of SMTs, while also exhibiting insufficient optimization capabilities. In this work, we propose a unified approach based on deep reinforcement learning (DRL) to address both RSMT and OSMT problems while generating diverse routing topologies. First, we design an edge point sequence (EPS) that leverages the structural characteristics of SMT to connect the output of the deep learning model with the SMT structure. Second, we propose a deep learning model tailored for EPS, employing the negative wirelength of SMT as a reward to train the model using DRL. Third, we provide a corresponding rapid and accurate wirelength computation algorithm for evaluating the quality of the construction solution to expedite model training. Finally, we leverage the stochastic nature of machine learning to construct diverse SMT construction solutions. To the best of our knowledge, this is the first unified approach capable of simultaneously addressing both RSMT and OSMT problems while generating diverse solutions. The proposed unified approach demonstrates superior solution quality and higher efficiency compared to specifically designed algorithms.

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors.While fairly successful, most existing works focus on the embedding techniques for static networks.But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks.To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks.Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp.Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling.Extensive experiments are conducted on four realworld datasets from three different areas.It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks.