Energy-Efficient Buffer-Based Ternary SRAM Cell With Application to Image Processing

Proposed abstract:

Ternary logic-based memory systems have gained growing importance in modern low-power computing applications such as Internet of Things (IoT), medical imaging, and portable electronic systems due to their ability to store and process more data per cell compared to conventional binary memories. The major advantages of ternary logic include reduced interconnection complexity, higher data density, and improved energy efficiency; however, these designs often face challenges of increased circuit area, poor noise margin stability, and higher power dissipation caused by voltage division networks and larger transistor counts. Existing Ternary Static Random-Access Memory (TSRAM) architectures such as 10T, 12T, 14T, and 16T cells show limitations in minimizing area and power while maintaining stability and speed. To overcome these issues, this work proposes a novel 8T Ternary SRAM cell designed and simulated using 22 nm CMOS technology in Tanner EDA. The proposed design eliminates redundant transistors and voltage division networks by introducing an optimized feedback-based latch structure that supports three stable logic levels (0, 1, and 2) with a single bit line configuration. This modification significantly reduces static and dynamic power consumption and achieves better stability compared to conventional ternary SRAMs. The novelty of the proposed design lies in its compact 8T configuration that offers improved area efficiency while maintaining low delay and strong data retention characteristics, making it suitable for embedded and IoT applications. Performance parameters such as read/write delay, power consumption, power-delay product (PDP), and static noise margin (SNM) are analysed and compared with existing designs through schematic and post-layout simulations using Tanner EDA tools. The results demonstrate that the proposed 8T TSRAM achieves superior energy efficiency and reduced area overhead, thereby offering a practical solution for next-generation high-density, low-power memory design.

Software Implementation:

• Modelsim & Xilinx

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A Compact 8T Ternary SRAM Architecture for Low-Power Applications in 22 nm CMOS Technology