IR drop analysis in the power delivery network (PDN) is crucial for the signoff of integrated circuit (IC) design. Static IR drop significantly affects the IC reliability. Machine learning (ML) has recently been applied to static IR drop prediction for its high accuracy and efficiency. However, most previous works cannot predict with unseen designs, and none can handle different technologies. These problems lead to long training times and data-gathering difficulties, making ML-based methods impractical in the industry. Therefore, a more applicable methodology for static IR drop predictions is needed. This paper proposes a fast, robust, highly technology-transferable image-to-image ML-based methodology for static IR-drop prediction. To enhance transferability and accuracy, we introduce a new input feature, layerwise maps, which encapsulates the PDN network topology well. We further derive a novel generic ML model for various designs and technologies with different numbers of PDN layers. Experimental results demonstrate our methodology's high accuracy, robustness, and technology transferability. We used only ten circuits to tune our pre-trained model on a new technology and achieved an average error rate of 10.4% IR drop value on unseen circuits. Additionally, we tuned our pre-trained model for the 2023 ICCAD CAD Contest. Compared to the contest winner, our method gets a comparable average error rate of 0.000152mV with a run time of less than 1.5 seconds and improves the MAE of the worst case by 29.7%.

This paper presents the first warpage-aware generative learningbased floorplanning algorithm to effectively model the warpage effect and optimize the die floorplan on a fixed outlined substrate. With more heterogeneous materials and dense interconnects in advanced packaging, warpage is a main reliability concern and may degrade system performance. We present a novel transformer-based encoding scheme to learn node and edge representations, followed by parallel decoding and warpage-aware legalization to jointly minimize die displacement and warpage. Experimental results show that our algorithm improves warpage by 9.9% and wirelength by 8.3% on average, compared with the state-of-the-art work.

Clustering single-bit flip-flops (SBFFs) into multi-bit flip-flops (MBFFs) effectively reduces power and area. However, excessive displacement during the clustering and legalization process may incur significant timing degradation. To address this issue, we propose the first comprehensive MBFF placement methodology that addresses excessive displacement caused by pre-placed cells during clustering and legalization while simultaneously optimizing timing, power, area, and bin utilization. Our methodology includes three main features: (1) a force model to relocate flip-flops and reduce timing violations, (2) a clustering and legalization process to reduce timing degradation caused by displacement, and (3) a multi-objective function to identify flip-flop candidates suitable for MBFF clustering. Our methodology outperforms all participating teams in the 2024 CAD Contest at ICCAD on Power and Timing Optimization Using Multi-Bit Flip-Flops, based on exactly the same settings.