Name
Abstract | ||
|---|---|---|
Scaling transistor size to the scale of the nanometer coupled with reduction of supply voltage has made SRAMs more vulnerable to soft errors than ever before. The vulnerability has been accentuated by increased variability in device parameters. In this paper, we present an analytical model for critical charge in order to assess the soft error vulnerability of 6T SRAM cell. The model takes into account the dynamic behavior of the cell and demonstrates a simple technique to decouple the nonlinearly coupled storage nodes. Decoupling of storage nodes enables solving associated current equations to determine the critical charge for an exponential noise current. The critical charge model thus developed consists of both NMOS and PMOS transistor parameters. Consequently, the model can estimate critical charge variations due to variability of transistor parameters and manufacturing defects, such as resistive contacts and vias. In addition, the model can serve as a tool to optimize the hibernation voltage of low-power SRAMs or the size ofMIMcapacitor per cell in order to achieve a target soft error robustness. Critical charge calculated by the model is in good agreement with SPICE simulations for a commercial 90-nmCMOS process with a maximum discrepancy of less than 5%. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TVLSI.2008.2003511 | IEEE Trans. VLSI Syst. |
Keywords | Field | DocType |
critical charge variation,critical charge,pmos transistor parameter,analytical model,storage node,critical charge model,nanometric srams,sram cell,target soft error robustness,soft error vulnerability,soft error,couplings,nmos transistor,sram,voltage,nonlinear equations,semiconductor device modeling | Soft error,NMOS logic,Spice,Computer science,Semiconductor device modeling,Voltage,Static random-access memory,Electronic engineering,Transistor,PMOS logic | Journal |
Volume | Issue | ISSN |
17 | 9 | 1063-8210 |
Citations | PageRank | References |
22 | 1.78 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shah M. Jahinuzzaman | 1 | 49 | 6.08 |
| Mohammad Sharifkhani | 2 | 147 | 25.76 |
| Manoj Sachdev | 3 | 669 | 88.45 |