Predictive Technology Model of Conventional CMOS Devices

Abstract

Bulk CMOS has been the dominant device structure for integrated circuit design during the past decades, because of its excellent scalability. It is expected that such a device type will continue toward the 10 nm regime. To efficiently predict the characteristics of future bulk CMOS, the scaling trends of primary model parameters, such as the threshold voltage and gate dielectric thickness, need to be identified; their association in determining major device characteristics should be well included for accurate model projection. In this chapter, a new generation of Predictive Technology Model (PTM) for conventional CMOS technology is presented to accomplish these goals. Based on a set of essential device models and early stage silicon data, PTM of bulk CMOS is successfully generated down to the 12 nm node. The accuracy of PTM predictions is comprehensively verified with published silicon data: the error of I on is below 10% for both NMOS and PMOS devices. By tuning only ten primary model parameters, PTM can be easily customized to cover a wide range of process uncertainties. Furthermore, PTM correctly captures the sensitivity to process variations. 2.1 PTM in Light of CMOS Scaling The relentless scaling of CMOS technology has accelerated in recent years and will arguably continue toward the 10 nm regime [1]. In the nanometer era, physical factors that previously had little or no impact on circuit performance are now becoming increasingly significant. Particular examples include process variations, transistor mobility degradation, and power consumption. These new effects pose dramatic challenges to robust circuit design and system integration. To continue the design success and make an impact on leading products, advanced circuit design exploration must start in parallel with, or even earlier than silicon development. This new design paradigm demands predictive MOSFET models that are reasonably accurate, scalable with main process and design knobs, and correctly capture those emerging physical effects. Y. Cao, Predictive Technology Model for Robust Nanoelectronic Design, Integrated Circuits and Systems,