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

A Study of Nanometer Semiconductor Scaling Effects on Microelectronics Reliability

The desire to assess the reliability of emerging scaled microelectronics technologies through faster reliability trials and more accurate acceleration models is the precursor for further research and experimentation in this relevant field. The effect of semiconductor scaling on microelectronics product reliability is an important aspect to the high reliability application user. From the perspective of a customer or user, who in many cases must deal with very limited, if any, manufacturer's reliability data to assess the product for a highly-reliable application, product-level testing is critical in the characterization and reliability assessment of advanced nanometer semiconductor scaling effects on microelectronics reliability. This dissertation provides a methodology on how to accomplish this and provides techniques for deriving the expected product-level reliability on commercial memory products. Competing mechanism theory and the multiple failure mechanism model are applied to two separate experiments; scaled SRAM and SDRAM products. Accelerated stress testing at multiple conditions is applied at the product level of several scaled memory products to assess the performance degradation and product reliability. Acceleration models are derived for each case. For several scaled SDRAM products, retention time degradation is studied and two distinct soft error populations are observed with each technology generation: early breakdown, characterized by randomly distributed weak bits with Weibull slope Beta=1, and a main population breakdown with an increasing failure rate. Retention time soft error rates are calculated and a multiple failure mechanism acceleration model with parameters is derived for each technology. Defect densities are calculated and reflect a decreasing trend in the percentage of random defective bits for each successive product generation. A normalized soft error failure rate of the memory data retention time in FIT/Gb and FIT/cm2 for several scaled SDRAM generations is presented revealing a power relationship. General models describing the soft error rates across scaled product generations are presented. The analysis methodology may be applied to other scaled microelectronic products and key parameters

Innovative Techniques for Testing and Diagnosing SoCs

We rely upon the continued functioning of many electronic devices for our everyday welfare, usually embedding integrated circuits that are becoming even cheaper and smaller with improved features. Nowadays, microelectronics can integrate a working computer with CPU, memories, and even GPUs on a single die, namely System-On-Chip (SoC). SoCs are also employed on automotive safety-critical applications, but need to be tested thoroughly to comply with reliability standards, in particular the ISO26262 functional safety for road vehicles. The goal of this PhD. thesis is to improve SoC reliability by proposing innovative techniques for testing and diagnosing its internal modules: CPUs, memories, peripherals, and GPUs. The proposed approaches in the sequence appearing in this thesis are described as follows: 1. Embedded Memory Diagnosis: Memories are dense and complex circuits which are susceptible to design and manufacturing errors. Hence, it is important to understand the fault occurrence in the memory array. In practice, the logical and physical array representation differs due to an optimized design which adds enhancements to the device, namely scrambling. This part proposes an accurate memory diagnosis by showing the efforts of a software tool able to analyze test results, unscramble the memory array, map failing syndromes to cell locations, elaborate cumulative analysis, and elaborate a final fault model hypothesis. Several SRAM memory failing syndromes were analyzed as case studies gathered on an industrial automotive 32-bit SoC developed by STMicroelectronics. The tool displayed defects virtually, and results were confirmed by real photos taken from a microscope. 2. Functional Test Pattern Generation: The key for a successful test is the pattern applied to the device. They can be structural or functional; the former usually benefits from embedded test modules targeting manufacturing errors and is only effective before shipping the component to the client. The latter, on the other hand, can be applied during mission minimally impacting on performance but is penalized due to high generation time. However, functional test patterns may benefit for having different goals in functional mission mode. Part III of this PhD thesis proposes three different functional test pattern generation methods for CPU cores embedded in SoCs, targeting different test purposes, described as follows: a. Functional Stress Patterns: Are suitable for optimizing functional stress during I Operational-life Tests and Burn-in Screening for an optimal device reliability characterization b. Functional Power Hungry Patterns: Are suitable for determining functional peak power for strictly limiting the power of structural patterns during manufacturing tests, thus reducing premature device over-kill while delivering high test coverage c. Software-Based Self-Test Patterns: Combines the potentiality of structural patterns with functional ones, allowing its execution periodically during mission. In addition, an external hardware communicating with a devised SBST was proposed. It helps increasing in 3% the fault coverage by testing critical Hardly Functionally Testable Faults not covered by conventional SBST patterns. An automatic functional test pattern generation exploiting an evolutionary algorithm maximizing metrics related to stress, power, and fault coverage was employed in the above-mentioned approaches to quickly generate the desired patterns. The approaches were evaluated on two industrial cases developed by STMicroelectronics; 8051-based and a 32-bit Power Architecture SoCs. Results show that generation time was reduced upto 75% in comparison to older methodologies while increasing significantly the desired metrics. 3. Fault Injection in GPGPU: Fault injection mechanisms in semiconductor devices are suitable for generating structural patterns, testing and activating mitigation techniques, and validating robust hardware and software applications. GPGPUs are known for fast parallel computation used in high performance computing and advanced driver assistance where reliability is the key point. Moreover, GPGPU manufacturers do not provide design description code due to content secrecy. Therefore, commercial fault injectors using the GPGPU model is unfeasible, making radiation tests the only resource available, but are costly. In the last part of this thesis, we propose a software implemented fault injector able to inject bit-flip in memory elements of a real GPGPU. It exploits a software debugger tool and combines the C-CUDA grammar to wisely determine fault spots and apply bit-flip operations in program variables. The goal is to validate robust parallel algorithms by studying fault propagation or activating redundancy mechanisms they possibly embed. The effectiveness of the tool was evaluated on two robust applications: redundant parallel matrix multiplication and floating point Fast Fourier Transform

NEGATIVE BIAS TEMPERATURE INSTABILITY STUDIES FOR ANALOG SOC CIRCUITS

Negative Bias Temperature Instability (NBTI) is one of the recent reliability issues in sub threshold CMOS circuits. NBTI effect on analog circuits, which require matched device pairs and mismatches, will cause circuit failure. This work is to assess the NBTI effect considering the voltage and the temperature variations. It also provides a working knowledge of NBTI awareness to the circuit design community for reliable design of the SOC analog circuit. There have been numerous studies to date on the NBTI effect to analog circuits. However, other researchers did not study the implication of NBTI stress on analog circuits utilizing bandgap reference circuit. The reliability performance of all matched pair circuits, particularly the bandgap reference, is at the mercy of aging differential. Reliability simulation is mandatory to obtain realistic risk evaluation for circuit design reliability qualification. It is applicable to all circuit aging problems covering both analog and digital. Failure rate varies as a function of voltage and temperature. It is shown that PMOS is the reliabilitysusceptible device and NBTI is the most vital failure mechanism for analog circuit in sub-micrometer CMOS technology. This study provides a complete reliability simulation analysis of the on-die Thermal Sensor and the Digital Analog Converter (DAC) circuits and analyzes the effect of NBTI using reliability simulation tool. In order to check out the robustness of the NBTI-induced SOC circuit design, a bum-in experiment was conducted on the DAC circuits. The NBTI degradation observed in the reliability simulation analysis has given a clue that under a severe stress condition, a massive voltage threshold mismatch of beyond the 2mV limit was recorded. Bum-in experimental result on DAC proves the reliability sensitivity of NBTI to the DAC circuitry

Investigation of the scalability limitations of phase change random access memory

Ph.DDOCTOR OF PHILOSOPH

Design-for-Test and Test Optimization Techniques for TSV-based 3D Stacked ICs

<p>As integrated circuits (ICs) continue to scale to smaller dimensions, long interconnects</p><p>have become the dominant contributor to circuit delay and a significant component of</p><p>power consumption. In order to reduce the length of these interconnects, 3D integration</p><p>and 3D stacked ICs (3D SICs) are active areas of research in both academia and industry.</p><p>3D SICs not only have the potential to reduce average interconnect length and alleviate</p><p>many of the problems caused by long global interconnects, but they can offer greater design</p><p>flexibility over 2D ICs, significant reductions in power consumption and footprint in</p><p>an era of mobile applications, increased on-chip data bandwidth through delay reduction,</p><p>and improved heterogeneous integration.</p><p>Compared to 2D ICs, the manufacture and test of 3D ICs is significantly more complex.</p><p>Through-silicon vias (TSVs), which constitute the dense vertical interconnects in a</p><p>die stack, are a source of additional and unique defects not seen before in ICs. At the same</p><p>time, testing these TSVs, especially before die stacking, is recognized as a major challenge.</p><p>The testing of a 3D stack is constrained by limited test access, test pin availability,</p><p>power, and thermal constraints. Therefore, efficient and optimized test architectures are</p><p>needed to ensure that pre-bond, partial, and complete stack testing are not prohibitively</p><p>expensive.</p><p>Methods of testing TSVs prior to bonding continue to be a difficult problem due to test</p><p>access and testability issues. Although some built-in self-test (BIST) techniques have been</p><p>proposed, these techniques have numerous drawbacks that render them impractical. In this dissertation, a low-cost test architecture is introduced to enable pre-bond TSV test through</p><p>TSV probing. This has the benefit of not needing large analog test components on the die,</p><p>which is a significant drawback of many BIST architectures. Coupled with an optimization</p><p>method described in this dissertation to create parallel test groups for TSVs, test time for</p><p>pre-bond TSV tests can be significantly reduced. The pre-bond probing methodology is</p><p>expanded upon to allow for pre-bond scan test as well, to enable both pre-bond TSV and</p><p>structural test to bring pre-bond known-good-die (KGD) test under a single test paradigm.</p><p>The addition of boundary registers on functional TSV paths required for pre-bond</p><p>probing results in an increase in delay on inter-die functional paths. This cost of test</p><p>architecture insertion can be a significant drawback, especially considering that one benefit</p><p>of 3D integration is that critical paths can be partitioned between dies to reduce their delay.</p><p>This dissertation derives a retiming flow that is used to recover the additional delay added</p><p>to TSV paths by test cell insertion.</p><p>Reducing the cost of test for 3D-SICs is crucial considering that more tests are necessary</p><p>during 3D-SIC manufacturing. To reduce test cost, the test architecture and test</p><p>scheduling for the stack must be optimized to reduce test time across all necessary test</p><p>insertions. This dissertation examines three paradigms for 3D integration - hard dies, firm</p><p>dies, and soft dies, that give varying degrees of control over 2D test architectures on each</p><p>die while optimizing the 3D test architecture. Integer linear programming models are developed</p><p>to provide an optimal 3D test architecture and test schedule for the dies in the 3D</p><p>stack considering any or all post-bond test insertions. Results show that the ILP models</p><p>outperform other optimization methods across a range of 3D benchmark circuits.</p><p>In summary, this dissertation targets testing and design-for-test (DFT) of 3D SICs.</p><p>The proposed techniques enable pre-bond TSV and structural test while maintaining a</p><p>relatively low test cost. Future work will continue to enable testing of 3D SICs to move</p><p>industry closer to realizing the true potential of 3D integration.</p>Dissertatio

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

COBE's search for structure in the Big Bang

The launch of Cosmic Background Explorer (COBE) and the definition of Earth Observing System (EOS) are two of the major events at NASA-Goddard. The three experiments contained in COBE (Differential Microwave Radiometer (DMR), Far Infrared Absolute Spectrophotometer (FIRAS), and Diffuse Infrared Background Experiment (DIRBE)) are very important in measuring the big bang. DMR measures the isotropy of the cosmic background (direction of the radiation). FIRAS looks at the spectrum over the whole sky, searching for deviations, and DIRBE operates in the infrared part of the spectrum gathering evidence of the earliest galaxy formation. By special techniques, the radiation coming from the solar system will be distinguished from that of extragalactic origin. Unique graphics will be used to represent the temperature of the emitting material. A cosmic event will be modeled of such importance that it will affect cosmological theory for generations to come. EOS will monitor changes in the Earth's geophysics during a whole solar color cycle