Statistical Characterization and Decomposition of SRAM cell Variability and Aging

abstract: Memories play an integral role in today's advanced ICs. Technology scaling has enabled high density designs at the price paid for impact due to variability and reliability. It is imperative to have accurate methods to measure and extract the variability in the SRAM cell to produce accurate reliability projections for future technologies. This work presents a novel test measurement and extraction technique which is non-invasive to the actual operation of the SRAM memory array. The salient features of this work include i) A single ended SRAM test structure with no disturbance to SRAM operations ii) a convenient test procedure that only requires quasi-static control of external voltages iii) non-iterative method that extracts the VTH variation of each transistor from eight independent switch point measurements. With the present day technology scaling, in addition to the variability with the process, there is also the impact of other aging mechanisms which become dominant. The various aging mechanisms like Negative Bias Temperature Instability (NBTI), Channel Hot Carrier (CHC) and Time Dependent Dielectric Breakdown (TDDB) are critical in the present day nano-scale technology nodes. In this work, we focus on the impact of NBTI due to aging in the SRAM cell and have used Trapping/De-Trapping theory based log(t) model to explain the shift in threshold voltage VTH. The aging section focuses on the following i) Impact of Statistical aging in PMOS device due to NBTI dominates the temporal shift of SRAM cell ii) Besides static variations , shifting in VTH demands increased guard-banding margins in design stage iii) Aging statistics remain constant during the shift, presenting a secondary effect in aging prediction. iv) We have investigated to see if the aging mechanism can be used as a compensation technique to reduce mismatch due to process variations. Finally, the entire test setup has been tested in SPICE and also validated with silicon and the results are presented. The method also facilitates the study of design metrics such as static, read and write noise margins and also the data retention voltage and thus help designers to improve the cell stability of SRAM.Dissertation/ThesisM.S. Electrical Engineering 201

Oxygen-insertion Technology for CMOS Performance Enhancement

Until 2003, the semiconductor industry followed Dennard scaling rules to improve complementary metal-oxide-semiconductor (CMOS) transistor performance. However, performance gains with further reductions in transistor gate length are limited by physical effects that do not scale commensurately with device dimensions: short-channel effects (SCE) due to gate-leakage-limited gate-oxide thickness scaling, channel mobility degradation due to enhanced vertical electric fields, increased parasitic resistances due to reductions in source/drain (S/D) contact area, and increased variability in transistor performance due to random dopant fluctuation (RDF) effects and gate work function variations (WFV). These emerging scaling issues, together with increased process complexity and cost, pose severe challenges to maintaining the exponential scaling of transistor dimensions. This dissertation discusses the benefits of oxygen-insertion (OI) technology, a CMOS performance booster, for overcoming these challenges. The benefit of OI technology to mitigate the increase in sheet resistance ($R_{sh}$) with decreasing junction depth ($X_J$) for ultra-shallow-junctions (USJs) relevant for deep-sub-micron planar CMOS transistors is assessed through the fabrication of $R_{sh}$ test structures, electrical characterization, and technology computer-aided design (TCAD) simulations. Experimental and secondary ion mass spectroscopy (SIMS) analyses indicate that OI technology can facilitate low-resistivity USJ formation by reducing $R_{sh}$ and $X_J$ due to retarded transient-enhanced-diffusion (TED) effects and enhanced dopant retention during post-implantation thermal annealing. It is also shown that a low-temperature-oxide (LTO) capping can increase $R_{sh}$ unfavorably due to lower dopant activation levels, which can be alleviated by OI technology. This dissertation extends the evaluation of OI technology to advanced FinFET technology, targeting 7/8-nm low power technology node. A bulk-Si FinFET design comprising a super-steep retrograde (SSR) fin channel doping profile achievable with OI technology is studied by three-dimensional (3-D) TCAD simulations. As compared with the conventional bulk-Si (control) FinFET design with a heavily-doped fin channel doping profile, SSR FinFETs can achieve higher $I_{on}/I_{off}$ ratios and reduce the sensitivity of device performance to variations due to the lightly doped fin channel. As compared with the SOI FinFET design, SSR FinFETs can achieve similarly low $V_{DD,min}$ for 6T-SRAM cell yield estimation. Both SSR and SOI design can provide for as much as 100 mV reduction in $V_{DD,min}$ compared with the control FinFET design. Overall, the SSR FinFET design that can be achieved with OI technology is demonstrated to be a cheaper alternative to the SOI FinFET technology for extending CMOS scaling beyond the 10-nm node. Finally, this dissertation investigates the benefits of OI technology for reducing the Schottky barrier height ($\Phi_{Bp}$) of a Pt/Ti/p-type Si metal-semiconductor (M/S) contact, which can be expected to help reduce the specific contact resistivity for a p-type silicon contact. Electrical measurements of back-to-back Schottky diodes, SIMS, and X-ray photoelectron spectroscopy (XPS) show that the reduction in $\Phi_{Bp}$ is associated with enhanced Ti 2p and Si 2p core energy level shifts. OI technology is shown to favor low-$\Phi_{Bp}$ Pt monosilicide formation during forming gas anneal (FGA) by suppressing the grain boundary diffusion of Pt atoms into the crystalline Si substrate

Nano-scale TG-FinFET: Simulation and Analysis

Transistor has been designed and fabricated in the same way since its invention more than four decades ago enabling exponential shrinking in the channel length. However, hitting fundamental limits imposed the need for introducing disruptive technology to take over. FinFET - 3-D transistor - has been emerged as the first successor to MOSFET to continue the technology scaling roadmap. In this thesis, scaling of nano-meter FinFET has been investigated on both the device and circuit levels. The studies, primarily, consider FinFET in its tri-gate (TG) structure. On the device level, first, the main TCAD models used in simulating electron transport are benchmarked against the most accurate results on the semi-classical level using Monte Carlo techniques. Different models and modifications are investigated in a trial to extend one of the conventional models to the nano-scale simulations. Second, a numerical study for scaling TG-FinFET according to the most recent International Technology Roadmap of Semiconductors is carried out by means of quantum corrected 3-D Monte Carlo simulations in the ballistic and quasi-ballistic regimes, to assess its ultimate performance and scaling behavior for the next generations. Ballisticity ratio (BR) is extracted and discussed over different channel lengths. The electron velocity along the channel is analyzed showing the physical significance of the off-equilibrium transport with scaling the channel length. On the circuit level, first, the impact of FinFET scaling on basic circuit blocks is investigated based on the PTM models. 256-bit (6T) SRAM is evaluated for channel lengths of 20nm down to 7nm showing the scaling trends of basic performance metrics. In addition, the impact of VT variations on the delay, power, and stability is reported considering die-to-die variations. Second, we move to another peer-technology which is 28nm FD-SOI as a comparative study, keeping the SRAM cell as the test block, more advanced study is carried out considering the cell‘s stability and the evolution from dynamic to static metrics

Solid State Circuits Technologies

The evolution of solid-state circuit technology has a long history within a relatively short period of time. This technology has lead to the modern information society that connects us and tools, a large market, and many types of products and applications. The solid-state circuit technology continuously evolves via breakthroughs and improvements every year. This book is devoted to review and present novel approaches for some of the main issues involved in this exciting and vigorous technology. The book is composed of 22 chapters, written by authors coming from 30 different institutions located in 12 different countries throughout the Americas, Asia and Europe. Thus, reflecting the wide international contribution to the book. The broad range of subjects presented in the book offers a general overview of the main issues in modern solid-state circuit technology. Furthermore, the book offers an in depth analysis on specific subjects for specialists. We believe the book is of great scientific and educational value for many readers. I am profoundly indebted to the support provided by all of those involved in the work. First and foremost I would like to acknowledge and thank the authors who worked hard and generously agreed to share their results and knowledge. Second I would like to express my gratitude to the Intech team that invited me to edit the book and give me their full support and a fruitful experience while working together to combine this book

Conquering Process Variability: A Key Enabler for Profitable Manufacturing in Advanced Technology Nodes

Abstract – Achieving the required time to market with economically acceptable yield levels and maintaining them in volume production has become a very challenging task in the most advanced technology nodes. One of the primary reasons is the relative increase in process variability in each generation. This paper will describe a comprehensive study of the main sources of variability and their effects on active devices, interconnect and ultimately product performance and yield. We will present benchmarking of yield loss components for different product classes. We will then propose several approaches for variability reduction in the design, yield ramp and volume manufacturing phases. EVOLUTION OF YIELD LOSS MECHANISMS In the older technology generations, manufacturing yield loss was dominated by random defects. By the time volum

6T-SRAM 1Mb Design with Test Structures and Post Silicon Validation

abstract: Static random-access memories (SRAM) are integral part of design systems as caches and data memories that and occupy one-third of design space. The work presents an embedded low power SRAM on a triple well process that allows body-biasing control. In addition to the normal mode operation, the design is embedded with Physical Unclonable Function (PUF) [Suh07] and Sense Amplifier Test (SA Test) mode. With PUF mode structures, the fabrication and environmental mismatches in bit cells are used to generate unique identification bits. These bits are fixed and known as preferred state of an SRAM bit cell. The direct access test structure is a measurement unit for offset voltage analysis of sense amplifiers. These designs are manufactured using a foundry bulk CMOS 55 nm low-power (LP) process. The details about SRAM bit-cell and peripheral circuit design is discussed in detail, for certain cases the circuit simulation analysis is performed with random variations embedded in SPICE models. Further, post-silicon testing results are discussed for normal operation of SRAMs and the special test modes. The silicon and circuit simulation results for various tests are presented.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Comprehensive Evaluation of Supply Voltage Underscaling in FPGA on-Chip Memories

In this work, we evaluate aggressive undervolting, i.e., voltage scaling below the nominal level to reduce the energy consumption of Field Programmable Gate Arrays (FPGAs). Usually, voltage guardbands are added by chip vendors to ensure the worst-case process and environmental scenarios. Through experimenting on several FPGA architectures, we measure this voltage guardband to be on average 39% of the nominal level, which in turn, delivers more than an order of magnitude power savings. However, further undervolting below the voltage guardband may cause reliability issues as the result of the circuit delay increase, i.e., start to appear faults. We extensively characterize the behavior of these faults in terms of the rate, location, type, as well as sensitivity to environmental temperature, with a concentration of on-chip memories, or Block RAMs (BRAMs). Finally, we evaluate a typical FPGA-based Neural Network (NN) accelerator under low-voltage BRAM operations. In consequence, the substantial NN energy savings come with the cost of NN accuracy loss. To attain power savings without NN accuracy loss, we propose a novel technique that relies on the deterministic behavior of undervolting faults and can limit the accuracy loss to 0.1% without any timing-slack overhead.Peer ReviewedPostprint (author's final draft

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