{"id":35947,"date":"2019-04-06T19:01:32","date_gmt":"2019-04-06T19:01:32","guid":{"rendered":"https:\/\/silvaco.com\/%e6%9c%aa%e5%88%86%e7%b1%bb\/six-sigma-analysis-of-digital-standard-cells\/"},"modified":"2019-04-06T19:01:32","modified_gmt":"2019-04-06T19:01:32","slug":"six-sigma-analysis-of-digital-standard-cells","status":"publish","type":"post","link":"https:\/\/silvaco.com\/zh-hans\/blog\/six-sigma-analysis-of-digital-standard-cells\/","title":{"rendered":"Six-Sigma Analysis of Digital Standard Cells"},"content":{"rendered":"
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Six-Sigma Analysis of Digital Standard Cells<\/h1>\n
A library of digital standard cells implements both Boolean logic and sequential storage functions. These foundation IPs are used extensively and repeatedly within application specific integrated circuits (ASICs), field programmable arrays (FPGAs), microprocessors and system-on-chip devices (SOCs). The behavior of these cells is affected by temperature and voltage conditions, as well as variability in the manufacture of the silicon die. With advanced nodes and low-power technologies comes the challenge for variation-aware characterization to assure that digital libraries will meet functionality and performance requirements. Depending on the final applications and the degree of replication, standard cells might need to be qualified up to six-sigma.<\/span>

The classic golden method to achieve reliable characterization has been Monte Carlo through intensive SPICE simulations. However, considering that libraries usually include dozens of types of standard cells that will be derived for different foundries, for different nodes, and for different process flavors, characterization under numerous process\/voltage\/temperature corners (PVTs) is a huge simulation burden that can take months, as shown in Figure 1.<\/span><\/div>\n
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Figure1. Digital library characterization with variations: can involve millions of simulations.<\/p><\/div><\/p>\n

High-sigma campaigns for digital library characterization require hundreds or thousands of CPUs and SPICE licenses across large and costly compute farms. The availability and cost of these resources to complete the necessary analysis is a serious difficulty for designers implementing such a flow. Monte Carlo characterization is accurate but has considerable disadvantages including being too time consuming to achieve enough precision and needing large computing resources. As a result, standard cell designers are looking for an effective and efficient solution for variation-aware digital library characterization. Ideally, changing only a single tool within their flow while preserving accuracy and precision would be desirable.<\/span>

Depending on the standard cell property to be characterized, measurements of interest are presented in specific formats as shown in Table 2:<\/span><\/p>\n