Accelerating Electromigration Aging: Fast Failure Detection for Nanometer ICs

For practical testing and detection of electromigration (EM) induced failures in dual damascene copper interconnects, one critical issue is creating stressing conditions to induce the chip to fail exclusively under EM in a very short period of time so that EM sign-off and validation can be carried out efficiently. Existing acceleration techniques, which rely on increasing temperature and current densities beyond the known limits, also accelerate other reliability effects making it very difficult, if not impossible, to test EM in isolation. In this article, we propose novel EM wear-out acceleration techniques to address the aforementioned issue. First we show that multi-segment interconnects with reservoir and sink structures can be exploited to significantly speedup the EM wear-out process. Based on this observation, we propose three strategies to accelerate EM induced failure: reservoir-enhanced acceleration, sink-enhanced acceleration, and a hybrid method that combines both reservoir and sink structures. We then propose several configurable interconnect structures that exploit atomic reservoirs and sinks for accelerated EM testing. Such configurable interconnect structures are very flexible and can be used to achieve significant lifetime reductions at the cost of some routing resources. Using the proposed technique, EM testing can be carried out at nominal current densities, and at a much lower temperature compared to traditional testing methods. This is the most significant contribution of this work since, to our knowledge, this is the only method that allows EM testing to be performed in a controlled environment without the risk of invoking other reliability effects that are also accelerated by elevated temperature and current density. Simulation results show that, using the proposed method, we can reduce the EM lifetime of a chip from 10 years down to a few hours 10^5X acceleration under the 150C temperature limit, which is sufficient for practical EM testing of typical nanometer CMOS ICs

A survey of carbon nanotube interconnects for energy efficient integrated circuits

This article is a review of the state-of-art carbon nanotube interconnects for Silicon application with respect to the recent literature. Amongst all the research on carbon nanotube interconnects, those discussed here cover 1) challenges with current copper interconnects, 2) process & growth of carbon nanotube interconnects compatible with back-end-of-line integration, and 3) modeling and simulation for circuit-level benchmarking and performance prediction. The focus is on the evolution of carbon nanotube interconnects from the process, theoretical modeling, and experimental characterization to on-chip interconnect applications. We provide an overview of the current advancements on carbon nanotube interconnects and also regarding the prospects for designing energy efficient integrated circuits. Each selected category is presented in an accessible manner aiming to serve as a survey and informative cornerstone on carbon nanotube interconnects relevant to students and scientists belonging to a range of fields from physics, processing to circuit design

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Design, Modeling and Analysis of Non-classical Field Effect Transistors

Transistor scaling following per Moore\u27s Law slows down its pace when entering into nanometer regime where short channel effects (SCEs), including threshold voltage fluctuation, increased leakage current and mobility degradation, become pronounced in the traditional planar silicon MOSFET. In addition, as the demand of diversified functionalities rises, conventional silicon technologies cannot satisfy all non-digital applications requirements because of restrictions that stem from the fundamental material properties. Therefore, novel device materials and structures are desirable to fuel further evolution of semiconductor technologies. In this dissertation, I have proposed innovative device structures and addressed design considerations of those non-classical field effect transistors for digital, analog/RF and power applications with projected benefits. Considering device process difficulties and the dramatic fabrication cost, application-oriented device design and optimization are performed through device physics analysis and TCAD modeling methodology to develop design guidelines utilizing transistor\u27s improved characteristics toward application-specific circuit performance enhancement. Results support proposed device design methodologies that will allow development of novel transistors capable of overcoming limitation of planar nanoscale MOSFETs. In this work, both silicon and III-V compound devices are designed, optimized and characterized for digital and non-digital applications through calibrated 2-D and 3-D TCAD simulation. For digital functionalities, silicon and InGaAs MOSFETs have been investigated. Optimized 3-D silicon-on-insulator (SOI) and body-on-insulator (BOI) FinFETs are simulated to demonstrate their impact on the performance of volatile memory SRAM module with consideration of self-heating effects. Comprehensive simulation results suggest that the current drivability degradation due to increased device temperature is modest for both devices and corresponding digital circuits. However, SOI FinFET is recommended for the design of low voltage operation digital modules because of its faster AC response and better SCEs management than the BOI structure. The FinFET concept is also applied to the non-volatile memory cell at 22 nm technology node for low voltage operation with suppressed SCEs. In addition to the silicon technology, our TCAD estimation based on upper projections show that the InGaAs FinFET, with superior mobility and improved interface conditions, achieve tremendous drive current boost and aggressively suppressed SCEs and thereby a strong contender for low-power high-performance applications over the silicon counterpart. For non-digital functionalities, multi-fin FETs and GaN HEMT have been studied. Mixed-mode simulations along with developed optimization guidelines establish the realistic application potential of underlap design of silicon multi-Fin FETs for analog/RF operation. The device with underlap design shows compromised current drivability but improve analog intrinsic gain and high frequency performance. To investigate the potential of the novel N-polar GaN material, for the first time, I have provided calibrated TCAD modeling of E-mode N-polar GaN single-channel HEMT. In this work, I have also proposed a novel E-mode dual-channel hybrid MIS-HEMT showing greatly enhanced current carrying capability. The impact of GaN layer scaling has been investigated through extensive TCAD simulations and demonstrated techniques for device optimization

Transistor Degradations in Very Large-Scale-Integrated CMOS Technologies

The historical evolution of hot carrier degradation mechanisms and their physical models are reviewed and an energy-driven hot carrier aging model is verified that can reproduce 62-nm-gate-long hot carrier degradation of transistors through consistent aging-parameter extractions for circuit simulation. A long-term hot carrier-resistant circuit design can be realized via optimal driver strength controls. The central role of the V GS ratio is emphasized during practical case studies on CMOS inverter chains and a dynamic random access memory (DRAM) word-line circuit. Negative bias temperature instability (NBTI) mechanisms are also reviewed and implemented in a hydrogen reaction-diffusion (R-D) framework. The R-D simulation reproduces time-dependent NBTI degradations interpreted into interface trap generation, Δ N it with a proper power-law dependency on time. The experimental evidence of pre-existing hydrogen-induced Si–H bond breakage is also proven by the quantifying R-D simulation. From this analysis, a low-pressure end-of-line (EOL) anneal can reduce the saturation level of NBTI degradation, which is believed to be caused by the outward diffusion of hydrogen from the gate regions and therefore prevents further breakage of Si–H bonds in the silicon-oxide interfaces

Cross-Layer Resiliency Modeling and Optimization: A Device to Circuit Approach

The never ending demand for higher performance and lower power consumption pushes the VLSI industry to further scale the technology down. However, further downscaling of technology at nano-scale leads to major challenges. Reduced reliability is one of them, arising from multiple sources e.g. runtime variations, process variation, and transient errors. The objective of this thesis is to tackle unreliability with a cross layer approach from device up to circuit level

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