Gauri Karve

A manufacturable dual channel (Si and SiGe) high-k metal gate CMOS technology with multiple oxides for high performance and low power applications

Band-gap engineering using SiGe channels to reduce the threshold voltage (VTH) in p-channel MOSFETs has enabled a simplified gate-first high-к/metal gate (HKMG) CMOS integration flow. Integrating Silicon-Germanium channels (cSiGe) on silicon wafers for SOC applications has unique challenges like the oxidation rate differential with silicon, defectivity and interface state density in the unoptimized state, and concerns with Tinv scalability. In overcoming these challenges, we show that we can leverage the superior mobility, low threshold voltage and NBTI of cSiGe channels in high-performance (HP) and low power (LP) HKMG CMOS logic MOSFETs with multiple oxides utilizing dual channels for nFET and pFET.

FINFET technology featuring high mobility SiGe channel for 10nm and beyond

SiGe for channel material has been explored as a major technology element after the introduction of FINFET into CMOS technology [1-4]. Research on long channel FETs and discrete short channel FETs demonstrated benefits in mobility [1-4] and reliability [2]. Given the disruption that SiGe FIN brings, every aspect associated with SiGe FIN needs to be carefully studied towards technology insertion. In this paper, we report the latest SiGe-based FINFET CMOS technology development. CMOS FINFETs with Si-FIN nFET and SiGe-FIN pFET is demonstrated as a viable technology solution for both server and mobile applications at 10nm node and beyond.

SiGe FinFET for practical logic libraries by mitigating local layout effect

SiGe FinFET has been explored for its benefit of high current drivability provided by channel strain [1-5]. We have demonstrated SiGe CMOS FinFET at 10nm technology ground rules including epitaxial defectivity control, DC performance and reliability benefit [6-8]. One concern of SiGe FinFET is channel strain relaxation by fin cut process [9] inducing local layout effect (LLE), which is crucial for product design. In this paper, we thoroughly examined LLE in SiGe pFinFET and explored its mitigation techniques. Two techniques are proposed and demonstrated successful LLE mitigation, which drives forward SiGe FinFET insertion to technology.

Technology viable DC performance elements for Si/SiGe channel CMOS FinFTT

Low Ge content SiGe-based CMOS FinFET is one of the promising technologies [1-2] offering solutions for both high performance and low power applications. In this paper, we established a competitive SiGe-based CMOS FinFET baseline and examined various elements for high performance offering. The performance elements in gate stack, channel doping, contact resistance, and junction have been explored to provide a cumulative 20% / 25% (n/pFET) performance enhancement. These elements provide a viable path towards performance enhancement for future technology nodes.

The importance of reporting both composite and maze yield for process split yield learning

Electrical composite yield for a given macro is calculated from many smaller macros that are called mazes. Maze yield can also be calculated. For defects that have a random distribution the composite and the maze yield always provide the same trend for process splits. Specifically the composite yield can be expressed by pk where p is the maze yield and k is the number of mazes. This is no longer true when systematic and random defects co-exist in the dataset. The reading of the composite yield alone can not provide sufficient information of the process impact. Here we provide five real-world case-studies where a complete picture of the electrical fail mechanism is obtained only by analyzing both the maze and the composite yield.

Analytic modeling of AC response to FET-level elements for CLY optimization

Minimizing circuit AC delay variations while maintaining power/performance is key for achieving high yielding products. The present work discusses an analytic model based approach for aligning the fundamental-FET electrical control and circuit-speed variability applied towards product screening. Such a model is proven to be effective in a manufacturing environment for predicting delay variation, and identifying the limiting process issues that drive our capability to achieve success in maximizing yield. The ability to understand circuit delay as it relates back to basic device measurements provides an ability to improve standard work in semiconductor manufacturing and realize continuous productivity enhancement.