Study of Static Noise Margin and Circuit Analysis on Advanced Technology Node SRAM Devices by Nanoprobing

2013 ◽  
Author(s):  
M.K. Dawood ◽  
T.H. Ng ◽  
P.K. Tan ◽  
H. Tan ◽  
S. James ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Case Studies ◽  
Circuit Analysis ◽  
Advanced Technology ◽  
Static Noise Margin ◽  
Tem Analysis ◽  
Technology Node ◽  
Noise Margin ◽  
The Individual ◽  
Static Noise

Abstract With further technology scaling, it becomes increasingly challenging for conventional methods of failure analysis (FA) to identify the cause of a failure. In this work, we present three case studies on the utilization of advanced nanoprobing for SRAM circuit analysis and fault identification on 20 nm technology node SRAM single bit devices. In the first 2 case studies, conventional failure analysis by passive voltage contrast (PVC) failed to identify any abnormality in the known failed bit. In the third case study, an abnormally bright PVC was observed by PVC inspection. In all three case studies, static noise margin of the SRAM bits during hold and read operations were performed to understand the circuit behavior of the failed bit cell. Next, nanoprobing on the individual transistors were performed to determine the failing transistor within the bit and the possible cause of the failure. TEM analysis was performed to identify and verify the failure mechanism.

Root Cause Analysis for Pin Leakage

2016 ◽  
Author(s):  
Zhigang Song ◽  
Jochonia Nxumalo ◽  
Manuel Villalobos ◽  
Sweta Pendyala
Keyword(s):  
Failure Analysis ◽  
Root Cause Analysis ◽  
Advanced Technology ◽  
Process Step ◽  
Cause Analysis ◽  
Hard Mask ◽  
Technology Node ◽  
Root Cause ◽  
Tools And Techniques

Abstract Pin leakage continues to be on the list of top yield detractors for microelectronics devices. It is simply manifested as elevated current with one pin or several pins during pin continuity test. Although many techniques are capable to globally localize the fault of pin leakage, root cause analysis and identification for it are still very challenging with today’s advanced failure analysis tools and techniques. It is because pin leakage can be caused by any type of defect, at any layer in the device and at any process step. This paper presents a case study to demonstrate how to combine multiple techniques to accurately identify the root cause of a pin leakage issue for a device manufactured using advanced technology node. The root cause was identified as under-etch issue during P+ implantation hard mask opening for ESD protection diode, causing P+ implantation missing, which was responsible for the nearly ohmic type pin leakage.

Design and Analysis of SRAM Cell using Negative Bit-Line Write Assist Technique and Separate Read Port for High-Speed Applications

2021 ◽  
pp. 2150270
Author(s):  
Jitendra Kumar Mishra ◽  
Lakshmi Likhitha Mankali ◽  
Kavindra Kandpal ◽  
Prasanna Kumar Misra ◽  
Manish Goswami
Keyword(s):  
Power Dissipation ◽  
High Speed ◽  
Random Access ◽  
Random Access Memory ◽  
Access Memory ◽  
Static Noise Margin ◽  
Semiconductor Memory ◽  
Noise Margin ◽  
Sram Cell ◽  
Static Noise

The present day electronic gadgets have semiconductor memory devices to store data. The static random access memory (SRAM) is a volatile memory, often preferred over dynamic random access memory (DRAM) due to higher speed and lower power dissipation. However, at scaling down of technology node, the leakage current in SRAM often increases and degrades its performance. To address this, the voltage scaling is preferred which subsequently affects the stability and delay of SRAM. This paper therefore presents a negative bit-line (NBL) write assist circuit which is used for enhancing the write ability while a separate (isolated) read buffer circuit is used for improving the read stability. In addition to this, the proposed design uses a tail (stack) transistor to decrease the overall static power dissipation and also to maintain the hold stability. The comparison of the proposed design has been done with state-of-the-art work in terms of write static noise margin (WSNM), write delay, read static noise margin (RSNM) and other parameters. It has been observed that there is an improvement of 48%, 11%, 19% and 32.4% in WSNM while reduction of 33%, 39%, 48% and 22% in write delay as compared to the conventional 6T SRAM cell, NBL, [Formula: see text] collapse and 9T UV SRAM, respectively.

scholarly journals Restoration circuits for Low power Reduce Swing of 6T and 8T SRAM Cell With Improved Read and Write Margins

2021 ◽  
Vol 7 ◽  
pp. 22-34
Author(s):  
Vinod Kumar ◽  
Ram Murti Rawat
Keyword(s):  
Low Power ◽  
Static Noise Margin ◽  
Noise Margin ◽  
Equivalent Time ◽  
Sram Cell ◽  
Section Viii ◽  
Static Noise

A paper that examines the factors thataffect the Static Noise Margin (SNM) of a StaticRandom Access memories. At an equivalent time,they specialise in optimizing Read and Writeoperation of 8T SRAM cell which is best than 6TSRAM cell Using Swing Restoration Dual NodeVoltage. The read and Write operation and improveStability analysis. This SRAM technique on thecircuit or architecture level is required to improveread and write operation. during this paperComparative Analysis of 6T and 8T SRAM Cellswith Improved Read and Write Margin is completedfor 180 nm Technology with Cadence Virtuososchematics Tool.This Paper is organized as follows: thecharacteristics of 6T SRAM cell are described arerepresented in section VIII. In section IX, proposed8T SRAM cell is described. In section X, Standard8T SRAM cell is described. Section XI includes thesimulation results which give comparison of variousparameters of 6T and 8T SRAM cells. In Section XIISimulation Results and DC analysis and sectionXIII conclusion the work.

scholarly journals Stability Improvement of an Efficient Graphene Nanoribbon Field-Effect Transistor-Based SRAM Design

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Mathan Natarajamoorthy ◽  
Jayashri Subbiah ◽  
Nurul Ezaila Alias ◽  
Michael Loong Peng Tan
Keyword(s):  
Power Consumption ◽  
Field Effect ◽  
Field Effect Transistor ◽  
Graphene Nanoribbon ◽  
Static Noise Margin ◽  
Noise Margin ◽  
Average Power Consumption ◽  
Sram Design ◽  
Effect Transistor ◽  
Static Noise

The development of the nanoelectronics semiconductor devices leads to the shrinking of transistors channel into nanometer dimension. However, there are obstacles that appear with downscaling of the transistors primarily various short-channel effects. Graphene nanoribbon field-effect transistor (GNRFET) is an emerging technology that can potentially solve the issues of the conventional planar MOSFET imposed by quantum mechanical (QM) effects. GNRFET can also be used as static random-access memory (SRAM) circuit design due to its remarkable electronic properties. For high-speed operation, SRAM cells are more reliable and faster to be effectively utilized as memory cache. The transistor sizing constraint affects conventional 6T SRAM in a trade-off in access and write stability. This paper investigates on the stability performance in retention, access, and write mode of 15 nm GNRFET-based 6T and 8T SRAM cells with that of 16 nm FinFET and 16 nm MOSFET. The design and simulation of the SRAM model are simulated in synopsys HSPICE. GNRFET, FinFET, and MOSFET 8T SRAM cells give better performance in static noise margin (SNM) and power consumption than 6T SRAM cells. The simulation results reveal that the GNRFET, FinFET, and MOSFET-based 8T SRAM cells improved access static noise margin considerably by 58.1%, 28%, and 20.5%, respectively, as well as average power consumption significantly by 97.27%, 99.05%, and 83.3%, respectively, to the GNRFET, FinFET, and MOSFET-based 6T SRAM design.

scholarly journals Modeling Static Noise Margin for FinFET based SRAM PUFs

2020 ◽  
Author(s):  
Shayesteh Masoumian ◽  
Georgios Selimis ◽  
Roel Maes ◽  
Geert-Jan Schrijen ◽  
Said Hamdioui ◽  
...  
Keyword(s):  
Static Noise Margin ◽  
Noise Margin ◽  
Static Noise

Read static noise margin aging model considering SBD and BTI effects for FinFET SRAMs

2016 ◽  
Vol 65 ◽  
pp. 20-26 ◽  
Author(s):  
Kolsoom Mehrabi ◽  
Behzad Ebrahimi ◽  
Roohollah Yarmand ◽  
Ali Afzali-Kusha ◽  
Hamid Mahmoodi
Keyword(s):  
Static Noise Margin ◽  
Aging Model ◽  
Noise Margin ◽  
Static Noise
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