Abstract
A Negative Capacitance Field effet transistor (NCFET) based Dual split control (DSC) 6T-SRAM cell has been designed and explored with Computing-in memory (CiM) architecture for energy efficient demonstration of Deep neural networks (DNN) basic operation such as Input-Weight (Dot) Product. The impact of ferro electric layer thickness (Tfe) on the SRAM cell perfomance metrics such as read noise margin (RNM), write noise margin (WNM) and energy efficiency for read and write operations have been analyzed at supply voltages of 0.3 V and 0.5 V. It has been observed that due to the steep slope characteristics, the NCFET based DSC 6T-SRAM cell design exhibits better RM, WM, and energy efficiency as compared to the baseline CMOS DSC SRAM cell design at VDD = 0.3 V and 0.5 V respectively (with Tfe range of 1 nm to 3 nm). Further, NCFET dual split control scheme for 6T-SRAM cell demonstrate improved read stability and write ability when compared with NCFET 6 T-SRAM cell design along with improved energy efficiency. NCFET based DSC 6T-SRAM CiM cell design has ∼22.77× and 12.41× lower energy consumption compared to the équivalent baseline 40 nm CMOS/baseline SRAM CiM design and ∼ 25.80× and 22.76× lower energy consumption compared to the NCFET based SRAM CiM at VDD = 0.3 V and 0.5 V respectively. NCFETs have improved steep subthreshold slope characteristics at an optimal Tfe value and NCFET SRAM based CiM circuits are expected to have higher noise margins and lower energy consumption compared to the baseline CMOS designs and are effective for NCFET based computing in-memory architectures with reduced read disturb issues in combination with DSC concept.
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