Ultra-Low-Power Embedded SRAM Design for Battery- Operated and Energy-Harvested IoT Applications

Internet of Things (IoT) devices such as wearable health monitors, augmented reality goggles, home automation, smart appliances, etc. are a trending topic of research. Various IoT products are thriving in the current electronics market. The IoT application needs such as portability, form factor, weight, etc. dictate the features of such devices. Small, portable, and lightweight IoT devices limit the usage of the primary energy source to a smaller rechargeable or non-rechargeable battery. As battery life and replacement time are critical issues in battery-operated or partially energy-harvested IoT devices, ultralow-power (ULP) system on chips (SoC) are becoming a widespread solution of chip makers’ choice. Such ULP SoC requires both logic and the embedded static random access memory (SRAM) in the processor to operate at very low supply voltages. With technology scaling for bulk and FinFET devices, logic has demonstrated to operate at low minimum operating voltages (VMIN). However, due to process and temperature variation, SRAMs have higher VMIN in scaled processes that become a huge problem in designing ULP SoC cores. This chapter discusses the latest published circuits and architecture techniques to minimize the SRAM VMIN for scaled bulk and FinFET technologies and improve battery life for ULP IoT applications.

[1]  Benton H. Calhoun,et al.  An Ultra-Low Energy Subthreshold SRAM Bitcell for Energy Constrained Biomedical Applications , 2014 .

[2]  Bruno Allard,et al.  Proposal of a new ultra low leakage 10T sub threshold SRAM bitcell , 2012, 2012 International SoC Design Conference (ISOCC).

[3]  Myeong-Eun Hwang,et al.  Process-Tolerant Ultralow Voltage Digital Subthreshold Design , 2008, 2008 IEEE Topical Meeting on Silicon Monolithic Integrated Circuits in RF Systems.

[4]  Benton H. Calhoun,et al.  A 130nm canary SRAM for SRAM dynamic write VMIN tracking across voltage, frequency, and temperature variations , 2015, 2015 IEEE Custom Integrated Circuits Conference (CICC).

[5]  E. Seevinck,et al.  Static-noise margin analysis of MOS SRAM cells , 1987 .

[6]  Kaushik Roy,et al.  A 32 kb 10T Sub-Threshold SRAM Array With Bit-Interleaving and Differential Read Scheme in 90 nm CMOS , 2009, IEEE Journal of Solid-State Circuits.

[7]  Zheng Guo,et al.  17.1 A 0.6V 1.5GHz 84Mb SRAM design in 14nm FinFET CMOS technology , 2015, 2015 IEEE International Solid-State Circuits Conference - (ISSCC) Digest of Technical Papers.

[8]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[9]  K. Roy,et al.  A 160 mV Robust Schmitt Trigger Based Subthreshold SRAM , 2007, IEEE Journal of Solid-State Circuits.

[10]  A.P. Chandrakasan,et al.  A 256 kb 65 nm 8T Subthreshold SRAM Employing Sense-Amplifier Redundancy , 2008, IEEE Journal of Solid-State Circuits.

[11]  Jiajing Wang,et al.  Techniques to Extend Canary-Based Standby $V_{DD}$ Scaling for SRAMs to 45 nm and Beyond , 2008, IEEE Journal of Solid-State Circuits.

[12]  J. Kwong,et al.  An Energy-Efficient Biomedical Signal Processing Platform , 2010, IEEE Journal of Solid-State Circuits.

[13]  Benton H. Calhoun,et al.  Dynamic write limited minimum operating voltage for nanoscale SRAMs , 2011, 2011 Design, Automation & Test in Europe.

[14]  Benton H. Calhoun,et al.  A 256kb 6T self-tuning SRAM with extended 0.38V–1.2V operating range using multiple read/write assists and VMIN tracking canary sensors , 2017, 2017 IEEE Custom Integrated Circuits Conference (CICC).

[15]  Ming-Hsien Tu,et al.  8T Single-ended sub-threshold SRAM with cross-point data-aware write operation , 2011, IEEE/ACM International Symposium on Low Power Electronics and Design.

[16]  Myeong-Eun Hwang Supply-Voltage Scaling Close to the Fundamental Limit Under Process Variations in Nanometer Technologies , 2011, IEEE Transactions on Electron Devices.

[17]  Marcel Baláz,et al.  MBIST for LEON3 processor core cache , 2013, 2013 IEEE 16th International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS).

[18]  Elena I. Vatajelu,et al.  SRAM soft error rate evaluation under atmospheric neutron radiation and PVT variations , 2013, 2013 IEEE 19th International On-Line Testing Symposium (IOLTS).

[19]  Benton H. Calhoun,et al.  Multiple Combined Write-Read Peripheral Assists in 6T FinFET SRAMs for Low-VMIN IoT and Cognitive Applications , 2018, ISLPED.

[20]  Jiajing Wang,et al.  Limits of bias based assist methods in nano-scale 6T SRAM , 2010, 2010 11th International Symposium on Quality Electronic Design (ISQED).

[21]  Benton H. Calhoun,et al.  A reverse write assist circuit for SRAM dynamic write VMIN tracking using canary SRAMs , 2014, Fifteenth International Symposium on Quality Electronic Design.

[22]  Benton H. Calhoun,et al.  A 1.3µW, 5pJ/cycle sub-threshold MSP430 processor in 90nm xLP FDSOI for energy-efficient IoT applications , 2016, 2016 17th International Symposium on Quality Electronic Design (ISQED).

[23]  Andrew R. Brown,et al.  Statistical variability in 14-nm node SOI FinFETs and its impact on corresponding 6T-SRAM cell design , 2012, 2012 Proceedings of the European Solid-State Device Research Conference (ESSDERC).

[24]  C. Van Hoof,et al.  Micropower energy harvesting , 2009, ESSDERC 2009.

[25]  A.P. Chandrakasan,et al.  Standby power reduction using dynamic voltage scaling and canary flip-flop structures , 2004, IEEE Journal of Solid-State Circuits.

[27]  E. Nowak,et al.  Low-power CMOS at Vdd = 4kT/q , 2001, Device Research Conference. Conference Digest (Cat. No.01TH8561).