Variability Analysis of On-chip Graphene Interconnects at Subthreshold Regime

In today's modern era, zest for low power applications and miniaturized gadgets has increased tremendously. Operating devices and circuits in subthreshold region of operation is an optimum technique to attain low power requirements in the system. On-chip interconnects that connect and facilitate signal transmission between devices and different modules as well as provide power and clock connections are one of the dominating parts of system. At deep submicron technologies, interconnects majorly affect and are deciding output performance parameter. To get higher performance, copper on-chip interconnects have been replaced by next-generation graphene interconnects. At miniaturized technology nodes, variation due to temperature, fabrication process and environmental fluctuations crops up significantly that varies the system output in on-chip ICs. As a result, variability analysis of on-chip interconnects at nano regime in subthreshold region has become need of the hour. In the present paper, effective variability analysis of graphene interconnect in subthreshold region is presented for the first time to the best of the knowledge of authors. Process corner, parametric and Monte-Carlo analyses have been performed to determine variability effect in on-chip multilayer graphene nanoribbon (MLGNR) interconnects. The different variability analyses have been performed at 32nm technology node.

[1]  Asen Asenov,et al.  Integrating intrinsic parameter fluctuation description into BSIMSOI to forecast sub-15 nm UTB SOI based 6T SRAM operation , 2006 .

[2]  S. Wind,et al.  Carbon nanotube electronics , 2003, Digest. International Electron Devices Meeting,.

[3]  Brajesh Kumar Kaushik,et al.  Process-Induced Delay Variation in SWCNT, MWCNT, and Mixed CNT Interconnects , 2015 .

[4]  James Tschanz,et al.  Impact of Parameter Variations on Circuits and Microarchitecture , 2006, IEEE Micro.

[5]  Yash Agrawal,et al.  Comprehensive Model for High-Speed Current-Mode Signaling in Next Generation MWCNT Bundle Interconnect Using FDTD Technique , 2016, IEEE Transactions on Nanotechnology.

[6]  William J. Bowhill,et al.  Design of High-Performance Microprocessor Circuits , 2001 .

[7]  Gaurav Soni,et al.  A Comparative Analysis of Copper and Carbon Nanotubes-Based Global Interconnects in 32 nm Technology , 2015, SocProS.

[8]  C. Xu,et al.  Modeling, Analysis, and Design of Graphene Nano-Ribbon Interconnects , 2009, IEEE Transactions on Electron Devices.

[9]  Y. Massoud,et al.  On the Impact of Process Variations for Carbon Nanotube Bundles for VLSI Interconnect , 2007, IEEE Transactions on Electron Devices.

[10]  Sani R. Nassif Design for Variability in DSM Technologies , 2000 .

[11]  Samik Raychaudhuri,et al.  Introduction to Monte Carlo simulation , 2008, 2008 Winter Simulation Conference.

[12]  Dimitrios Soudris,et al.  Designing Cmos Circuits For Low Power , 2011 .

[13]  Bo Zhai,et al.  A Variation-Tolerant Sub-200 mV 6-T Subthreshold SRAM , 2008, IEEE Journal of Solid-State Circuits.

[14]  Rajeevan Chandel,et al.  Dynamic Crosstalk Analysis in Coupled Interconnects for Ultra-Low Power Applications , 2015, Circuits Syst. Signal Process..

[15]  Fei Yuan,et al.  CMOS Current-Mode Circuits for Data Communications (Analog Circuits and Signal Processing) , 2006 .

[16]  Franz Kreupl,et al.  Nanoelectronics Based on Carbon Nanotubes: Technological Challenges and Recent Developments , 2005 .

[17]  Yash Agrawal,et al.  Variability Analysis of Stochastic Parameters on the Electrical Performance of On-Chip Current-Mode Interconnect System , 2017 .

[18]  Wayne P. Burleson,et al.  Robust multi-level current-mode on-chip interconnect signaling in the presence of process variations , 2005, Sixth international symposium on quality electronic design (isqed'05).

[19]  Sani R. Nassif,et al.  High Performance CMOS Variability in the 65nm Regime and Beyond , 2006, 2007 IEEE International Electron Devices Meeting.

[20]  Mathias Beike,et al.  Digital Integrated Circuits A Design Perspective , 2016 .

[21]  Lizy Kurian John,et al.  A novel memory bus driver/receiver architecture for higher throughput , 1998, Proceedings Eleventh International Conference on VLSI Design.

[22]  Massimo Alioto,et al.  Analysis and Characterization of Variability in Subthreshold Source-Coupled Logic Circuits , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[23]  V. Tucci,et al.  Impact of the Variability of the Process Parameters on CNT-Based Nanointerconnects Performances: A Comparison Between SWCNTs Bundles and MWCNT , 2012, IEEE Transactions on Nanotechnology.

[24]  Brajesh Kumar Kaushik,et al.  Delay uncertainty in MLGNR interconnects under process induced variations of width, doping, dielectric thickness and mean free path , 2014 .

[25]  James Tschanz,et al.  Parameter variations and impact on circuits and microarchitecture , 2003, Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451).