BEOL with airgap technology is an alternative metallization option with promising performance, electrical yield and reliability to explore at 2nm node and beyond. Airgaps form cavities in inter-metal dielectrics (IMD) between interconnects. The ultra-low dielectric constant reduces line-to-line capacitance, thus shortening the interconnect delay. The shortened interconnect delay is beneficial to setup timing but harmful to hold timing. To minimize the additional manufacturing cost, the number of metal layers that accommodate airgaps is practically limited. Hence, circuit timing optimization at post routing can be achieved by wisely performing airgap insertion and layer reassignment to timing critical nets. In this paper, we present a novel and fast airgap insertion approach for timing optimization. A Slack Dependency Graph (SDG) is constructed to view the timing slack relationship of a circuit with path segments. With the global view provided by SDG, we can avoid ineffective optimizations. Our Linear Programming (LP) formulation simultaneously solves airgap insertion and layer reassignment and allows a flexible amount of airgap to be inserted. Both SDG update and LP solving can be done extremely fast. Experimental results show that our approach outperforms the state-of-the-art work on both total negative slack (TNS) and worst negative slack (WNS) with more than 89× speedup.

As modern photolithography feature sizes continue to shrink, sub-resolution assist feature (SRAF) generation has become a key resolution enhancement technique to improve the manufacturing process window. State-of-the-art works resort to machine learning to overcome the deficiencies of model-based and rule-based approaches. Nevertheless, these machine learning-based methods do not consider or implicitly consider the optical interference between SRAFs, and highly rely on post-processing to satisfy SRAF mask manufacturing rules. In this paper, we are the first to generate SRAFs using reinforcement learning to address SRAF interference and produce mask-rule-compliant results directly. In this way, our two-phase learning enables us to emulate the style of model-based SRAFs while further improving the process variation (PV) band. A state alignment and action transformation mechanism is proposed to achieve orientation equivariance while expediting the training process. We also propose a transfer learning framework, allowing SRAF generation under different light sources without retraining the model. Compared with state-of-the-art works, our method improves the solution quality in terms of PV band and edge placement error (EPE) while reducing the overall runtime.

Time-division multiplexing (TDM) is widely used to overcome bandwidth limitations and thus enhances routability in multi-FPGA systems due to the shortage of I/O pins in an FPGA. However, multiplexed signals induce significant delays. To evaluate timing degradation, nets with similar criticalities are often grouped to form NetGroups. In this paper, we propose a framework concerning routing topology and time-division multiplexing co-optimization for multi-FPGA systems. The proposed framework first generates high-quality topologies considering Net-Group criticalities. Then, inspired by column generation, TDM ratio assignment is solved optimally by Lagrangian relaxation. Experimental results show that our approach outperforms the top three entries of ICCAD 2019 CAD Contest. Moreover, our TDM ratio assignment algorithm can further improve the results of the top three winners to almost as good as ours.