Work

Research on expansion of E-state bias current margin in ERSFQ circuit

Year: 2025

Type: article

Abstract: As a member of the superconducting integrated circuit family, energy-efficient rapid single flux quantum (ERSFQ) circuit combines low-power consumption with high frequency, showing promise for application in high-energy efficient neuromorphic computing and traditional high-performance computing. Previous studies have demonstrated that there are typically two energy dissipation states in working ERSFQ circuits: energy-efficient SFQ state (E-state) and resistive-biased SFQ state (R-state). These states occur at different biasing current ranges, and the sum of the bias margin of these two states is close to the margin of rapid single flux quantum (RSFQ) circuits of the same design. For ultra-low-power applications, the bias current margin of the R-state must be excluded, leading to a substantially narrower bias margin in ERSFQ circuits than in their RSFQ counterparts. In our previous research, we proposed the feeding pulse method to increase the bias current margin by extending the lower boundary of the E-state through the injection of feeding pulses into the feeding Josephson transmission line (FJTL). In this study, we further investigated the current distribution mechanism in ERSFQ circuits and, based on this, proposed a method of optimizing design of the FJTL to redirect excess current to the FJTL, so as to expand the upper boundary of the E-state’s bias current margin. We tested an ERSFQ 8-bit shift register of the ERSFQ by applying methods proposed in this work together with the feeding pulses method proposed before. The results demonstrated that the bias current margin of the E-state expanded from (94%, 98%) to (56%, 116%), corresponding to a 14-times increase, with both the lower and upper boundary successfully extended. Our research addresses the issue of limited current bias margin in ERSFQ circuits during actual operation, providing a more robust ERSFQ technology for future applications in neural networks and high-performance computing.

Source: Superconductor Science and Technology

DOI: 10.1088/1361-6668/adf891

Authors: Yujian Ding, Bicong Weng, Xue Zhang, Shucheng Yang, Jie Ren

Institutions: Chinese Academy of Sciences, Shanghai Institute of Microsystem and Information Technology, University of Chinese Academy of Sciences

Language: English

Topic: Superconducting and THz Device Technology

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,325

Funder: National Natural Science Foundation of China