A reliable, multi-bit error tolerant 11T SRAM memory design for wireless sensor nodes
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A reliable, multi-bit error tolerant 11T SRAM memory design for wireless sensor nodes Vishal Sharma1 • Neha Gupta1 • Ambika Prasad Shah1 • Santosh Kumar Vishvakarma1 Shailesh Singh Chouhan2
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Received: 19 December 2019 / Revised: 11 April 2020 / Accepted: 26 September 2020 Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The work proposes an 11T SRAM cell which confirms its reliability for Internet of Things (IoT) based health monitoring system. The cell executes improved write and read ability using data-dependent feedback cutting and read decoupled access path mechanism respectively. The write and read stabilities of proposed cell are 2:67 and 1:98 higher than the conventional 6T cell with 1:53 area overhead. Moreover, the improved soft error tolerance and better reliability against negative bias temperature instability (NBTI) of proposed 11T SRAM cell as compared to other considered cells make it suitable for the bio medical implant. A low-power double adjacent bit error detection and correction (DAEDC) scheme is proposed to further improve the robustness of designed 1 Kb bit-interleaved memory against the soft error occurrence. The leakage power of proposed cell is controlled by the stacking devices used in its cross-coupled inverter pair and the column based read ground signal (RGND) further controls the unnecessary bit line switching power of the array. Keywords Static random access memory (SRAM) Stability Half-select issue Bit-interleaving Soft error
1 Introduction The intensive growth of internet market has fueled the emergence of Internet of Things (IoT) in our day life, ways of working, and business. This plethora of connected things work coherently using the Wireless Sensor Networks
& Santosh Kumar Vishvakarma [email protected] Vishal Sharma [email protected] Neha Gupta [email protected] Ambika Prasad Shah [email protected] Shailesh Singh Chouhan [email protected] 1
Nanoscale Devices, VLSI Circuit and System Design Lab, Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, M.P. 453552, India
2
EIS Lab, Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea˚, Sweden
(WSNs) that facilitate the connectivity, communication and data gathering [1]. Based on the network range, WSNs are typically categorized into four classes: personal area networks (PANs), local area networks (LANs), neighbourhood area networks (NANs) and wide area networks (WANs) [2]. With the involvement of IoT in run-time health monitoring system named as digital health system, the use of PANs or Body Sensor Networks (BSN) is quite common. A typical BSN is depicted in Fig. 1(a) where the various sensors are monitoring the physiological signals from a human subject [3, 4]. The sensed data is then transmitted to the nearest medical center through the base station [5]. As can be seen from Fig. 1(b), each of such batteryoperated sensor node [6] captures, process and temporarily stores t
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