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Resumen de Algorithms and methodologies for lnterconnect reliability analysis of lntegrated circuits

Palkesh Jain

  • The phenomenal progress of computing devices has been largely made possible by the sustained efforts of semiconductor industry in innovating techniques for extremely large scale integration. Indeed, gigantically-integrated-circuits today contain multi-billion interconnects which enable the transistors to talk to each other ¿ all in a space of few mm2. Such aggressively downscaled components (transistors and interconnects) silently suffer from increasing electric fields and impurities/defects during manufacturing. Compounded by the Gigahertz switching, the challenges of reliability and design integrity remains very much alive for chip designers, with Electromigration (EM) being the foremost interconnect reliability challenge.

    Traditionally, EM containment revolves around EM guidelines, generated at single-component level, whose non-compliance means that the component fails. Failure usually refers to deformation due to EM ¿ manifested in form of resistance increase, which is unacceptable from circuit performance point of view. Subsequent aspects deal with correct-by-construct design of the chip followed by the signoff-verification of EM reliability. Interestingly, chip designs today have reached a dilemma point of reduced margin between the actual and reliably-allowed current densities, versus, comparatively scarce system-failures. Consequently, this research is focused on improved algorithms and methodologies for interconnect reliability analysis enabling accurate and design-specific interpretation of EM events.

    In the first part, we present a new methodology for logic-IP (cell) internal EM verification: an inadequately attended area in the literature. Our SPICE-correlated model helps in evaluating the cell lifetime under any arbitrary reliability speciations, without generating additional data ¿ unlike the traditional approaches. The model is apt for today's fab less eco-system, where there is a) increasing reuse of standard cells optimized for one market condition to another (e.g., wireless to automotive), as well as b) increasing 3rd party content on the chip requiring a rigorous sign-off. We present results from a 28nm production setup, demonstrating significant violations relaxation and flexibility to allow runtime level reliability retargeting.

    Subsequently, we focus on an important aspect of connecting the individual component-level failures to that of the system failure. We note that existing EM methodologies are based on serial reliability assumption, which deems the entire system to fail as soon as the first component in the system fails. With a highly redundant circuit topology ¿ that of a clock grid ¿ in perspective, we present algorithms for EM assessment, which allow us to incorporate and quantify the benefit from system redundancies. With the skew metric of clock-grid as a failure criterion, we demonstrate that unless such incorporations are done, chip lifetimes are underestimated by over 2x.

    This component-to-system reliability bridge is further extended through an extreme order statistics based approach, wherein, we demonstrate that system failures can be approximated by an asymptotic kth-component failure model, otherwise requiring costly Monte Carlo simulations. Using such approach, we can efficiently predict a system-criterion based time to failure within existing EDA frameworks.

    The last part of the research is related to incorporating the impact of global/local process variation on current densities as well as fundamental physical factors on EM. Through Hermite polynomial chaos based approach, we arrive at novel variations-aware current density models, which demonstrate significant margins (> 30%) in EM lifetime when compared with the traditional worst case approach.The above research problems have been motivated by the decade-long work experience of the author dealing with reliability issues in industrial SoCs, first at Texas Instruments and later at Qualcomm.


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