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Design of High Speed Sense Amplifiers for SRAM IC

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In today's tech-driven landscape, semiconductor chips are critical to the functionality of most modern devices, requiring compact designs and low power consumption for efficient data storage and memory. SRAM (Static Random Access Memory) is key to meeting these demands. This study leverages Cadence Virtuoso software to design a high-performance sense amplifier circuit specifically tailored for low-power SRAM applications. Various power reduction strategies were explored, resulting in an optimized solution within a redesigned SRAM architecture. The study analyzes the impact of power consumption and response time of the proposed sense amplifier by adjusting key parameters, such as the transistor width-to-length (W/L) ratio, power supply, and nanoscale technology. Detailed metrics on power usage and transistor count for different configurations are presented to identify the most effective approach. Our proposed low-power sense amplifier design shows promising results, incorporating three VLSI power reduction techniques to enhance efficiency. These innovations in low-power SRAM are poised to advance memory-centric neuromorphic computing applications.
Title: Design of High Speed Sense Amplifiers for SRAM IC
Description:
In today's tech-driven landscape, semiconductor chips are critical to the functionality of most modern devices, requiring compact designs and low power consumption for efficient data storage and memory.
SRAM (Static Random Access Memory) is key to meeting these demands.
This study leverages Cadence Virtuoso software to design a high-performance sense amplifier circuit specifically tailored for low-power SRAM applications.
Various power reduction strategies were explored, resulting in an optimized solution within a redesigned SRAM architecture.
The study analyzes the impact of power consumption and response time of the proposed sense amplifier by adjusting key parameters, such as the transistor width-to-length (W/L) ratio, power supply, and nanoscale technology.
Detailed metrics on power usage and transistor count for different configurations are presented to identify the most effective approach.
Our proposed low-power sense amplifier design shows promising results, incorporating three VLSI power reduction techniques to enhance efficiency.
These innovations in low-power SRAM are poised to advance memory-centric neuromorphic computing applications.

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