SRAM Cell Model Generation and Modeling Efficiency Take Center Stage in New Software Releases

Blog Post created by kaelly_farnham Employee on May 4, 2017

Accurate and efficient modeling is critical to successful design, especially when it comes to the Static Random Access Memory (SRAM) cell, the minimum geometry devices in integrated circuit technology. Modeling such circuits has grown increasingly complex with the advent of nanometer scale process geometries. That’s because increasing process variation makes model stability more challenging.


The latest release of Keysight Technologies’ Model Builder Program (MBP) 2017 now features a SRAM cell model generation package that’s designed to address this challenge head on, by enabling engineers to extract transistor-level and memory-cell models in one MBP session. The user can easily simulate cell-level figures-of-merit, tune model parameters and even compare two memory cell models (Figure 1).

 Comparison of two SRAM cell models, MPB 2017


Figure 1. With MBP 2017, users can easily compare SRAM cell models.


According to Roberto Tinti, Keysight’s Device Modeling Planning Manager, the extraction package came about as a result of a collaboration with a major customer. “Working together we developed a solution that not only reduces modeling iteration but cuts the design cycle as well. It promises to bring many benefits to both existing and future MBP customers.”


Additional enhancements in MBP 2017 include:

  • An enhanced statistical model extraction flow and updated application examples
  • Enhanced extraction flows for BSIM3v3, BSIM4, and BSIM-CMG
  • Updates to the following models: BSIM-CMG 110.0, BSIM-CMG 109.0, BSIM-IMG 102.8, BSIM-IMG 102.7, HiSIM2 2.9.0, HiSIM_HV 2.3.2, HiSIM_HV 2.3.1, HiSIM_HV 2.3.0, EKV 302.00


scripts-based model extraction flow. MBP 2017

Figure 2. Available in MBP 2017 is an updated scripts-based model extraction flow. 


Keysight has also released a new version of its Model Quality Assurance (MQA) 2017 software with enhancements designed to improve modeling efficiency and model quality. MQA 2017 contains a new internal SPICE3 engine that allows users to run quick simulation and quality assurance (QA). It supports the latest compact model versions. Python scripts are also now supported, enabling generation of user-defined Excel tables based on exciting QA results.


"The advanced effects and parasitics in new devices make device modeling more complicated than ever,” said MA Long, Device Modeling Product Manager with Keysight. “With the new internal engine, users can run model quality checks during parameter extraction and uncover potential risks in the early design stage. Support for Python scripts provides the user even more flexibility and functionality in generating tables over the existing TCL and Perl solutions offered."

 compare table generation with python script, MQA 2017

 Figure 3. N/P compare table generation with Python Script as provided by MQA 2017. 


Other enhancements in MQA 2017 include support for Spectre native aging simulation, SmartSpice version 4.26.7.R and Microsoft Office 2016. Unlike traditional manual scripting methods, MQA enables users to check their SPICE models, compare models and generate QA reports in a complete and efficient way.


MBP and MQA are Keysight’s industry-leading device modeling and characterization products. MQA is the industry standard for SPICE model acceptance and sign off, and is widely adopted by leading integrated device manufacturers (IDMs), foundries and design houses. Information on MBP 2017 and MQA 2017, is available at www.keysight.com/find/eesof-mbp2017 and www.keysight.com/find/eesof-mqa2017, respectively. To apply for a free software trial, go to www.keysight.com/find/mytrial.mbp.blg and www.keysight.com/find/mytrial.mqa.blg.


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