DRV Evaluation of 6T SRAM Cell Using Efficient Optimization Techniques

Vinod Kumar Joshi, Chetana Nayak

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


An optimization based method which uses bisection search algorithm has been proposed to evaluate the accurate value of Data Retention Voltage (DRV) of a 6T Static Random Access Memory (SRAM) cell using 45 nm technology in the presence of process parameter variations. Further, we incorporate an Artificial Neural Network (ANN) block in our proposed methodology to optimize the simulation run time. The highest values obtained from these two methods are declared as the DRV. We noted an increase in DRV with temperature (T) and process variations (PVs). The main advantage of the proposed technique is to reduce the DRV evaluation time and for our case, we observe improvement in evaluation time of DRV by ≈46, ≈27, and ≈8 times at 25°C for 3 σ, 4 σ, and 5 σ variations, respectively, using ANN block to without using ANN block.

Original languageEnglish
Article number3457284
JournalActive and Passive Electronic Components
Publication statusPublished - 01-01-2018

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering


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