LAUSR

With the emergence of large-scale single-flux-quantum (SFQ) circuits, it is desirable to offload heavy routing tasks to automated electronic design automation (EDA) tools. The unique characteristic of SFQ circuits is that large clock distribution networks are generally required to pass the clock signal to clocked cells subject to limited fanout drive capability of the intervening clock buffers. This scenario results in a huge challenge to control the clock skew and minimize the signal path delays in an SFQ circuit during the routing step. Values of the clock skew and path delays determine not only the maximum working frequencies but also whether or not there are hold time violations. However, it is not necessary to develop a timing-driven routing tool specifically for SFQ circuits from scratch because the number of the paths that set the maximum clock frequencies or give rise to hold time violations is generally very small. Thus, we present a novel postrouting optimization framework that augments standard maze routing tools by adding the capability of reducing path lengths to maximize the working frequency of the chip and meandering paths to avoid hold time violations. A framework is developed in which machine learning is applied to analyze wire distributions and a maze routing algorithm is used to reroute targeted paths. Based on the MIT-LL SFQ5ee process technology, we demonstrate that our framework can improve the minimum working frequency by 7$\%$ on average over the state-of-the-art EDA routing tool for a suite of 14 SFQ circuits while fixing all hold time violations.