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

DFM Techniques for the Detection and Mitigation of Hotspots in Nanometer Technology

With the continuous scaling down of dimensions in advanced technology nodes, process variations are getting worse for each new node. Process variations have a large influence on the quality and yield of the designed and manufactured circuits. There is a growing need for fast and efficient techniques to characterize and mitigate the effects of different sources of process variations on the design's performance and yield. In this thesis we have studied the various sources of systematic process variations and their effects on the circuit, and the various methodologies to combat systematic process variation in the design space. We developed abstract and accurate process variability models, that would model systematic intra-die variations. The models convert the variation in process into variation in electrical parameters of devices and hence variation in circuit performance (timing and leakage) without the need for circuit simulation. And as the analysis and mitigation techniques are studied in different levels of the design ow, we proposed a flow for combating the systematic process variation in nano-meter CMOS technology. By calculating the effects of variability on the electrical performance of circuits we can gauge the importance of the accurate analysis and model-driven corrections. We presented an automated framework that allows the integration of circuit analysis with process variability modeling to optimize the computer intense process simulation steps and optimize the usage of variation mitigation techniques. And we used the results obtained from using this framework to develop a relation between layout regularity and resilience of the devices to process variation. We used these findings to develop a novel technique for fast detection of critical failures (hotspots) resulting from process variation. We showed that our approach is superior to other published techniques in both accuracy and predictability. Finally, we presented an automated method for fixing the lithography hotspots. Our method showed success rate of 99% in fixing hotspots

Electrical Design for Manufacturability Solutions: Fast Systematic Variation Analysis and Design Enhancement Techniques

The primary objectives in this research are to develop computer-aided design (CAD) tools for Design for Manufacturability (DFM) solutions that enable designers to conduct more rapid and more accurate systematic variation analysis, with different design enhancement techniques. Four main CAD tools are developed throughout my thesis. The first CAD tool facilitates a quantitative study of the impact of systematic variations for different circuits' electrical and geometrical behavior. This is accomplished by automatically performing an extensive analysis of different process variations (lithography and stress) and their dependency on the design context. Such a tool helps to explore and evaluate the systematic variation impact on any type of design. Secondly, solutions in the industry focus on the "design and then fix philosophy", or "fix during design philosophy", whereas the next CAD tool involves the "fix before design philosophy". Here, the standard cell library is characterized in different design contexts, different resolution enhancement techniques, and different process conditions, generating a fully DFM-aware standard cell library using a newly developed methodology that dramatically reduce the required number of silicon simulations. Several experiments are conducted on 65nm and 45nm designs, and demonstrate more robust and manufacturable designs that can be implemented by using the DFM-aware standard cell library. Thirdly, a novel electrical-aware hotspot detection solution is developed by using a device parameter-based matching technique since the state-of-the-art hotspot detection solutions are all geometrical based. This CAD tool proposes a new philosophy by detecting yield limiters, also known as hotspots, through the model parameters of the device, presented in the SPICE netlist. This novel hotspot detection methodology is tested and delivers extraordinary fast and accurate results. Finally, the existing DFM solutions, mainly address the digital designs. Process variations play an increasingly important role in the success of analog circuits. Knowledge of the parameter variances and their contribution patterns is crucial for a successful design process. This information is valuable to find solutions for many problems in design, design automation, testing, and fault tolerance. The fourth CAD solution, proposed in this thesis, introduces a variability-aware DFM solution that detects, analyze, and automatically correct hotspots for analog circuits

SISTEMA DE DETECCIÓN DE PUNTOS DE ACCESO WIFI EN UN CENTRO DE DATOS

Se presenta la implantación de un sistema que detecta puntos de acceso WiFi no autorizados en un centro de datos que puedan vulnerar la seguridad de la información. El sistema consiste de tres módulos: el módulo de detección WiFi, el módulo supervisor y la interface de usuario. Se construyeron cinco módulos de detección WiFi instalados en diferentes puntos del centro de datos los cuales se comunican inalámbricamente con el módulo supervisor conectado a una computadora donde se ubica la interface de usuario. La computadora se localiza en una oficina de control y monitoreo. Periódicamente la interface de usuario solicita al módulo supervisor, conectado al puerto USB de la computadora, transmitir la orden de exploración de señal WiFi a cada módulo de detección, al recibir esta orden los módulos envían el nombre de los puntos de acceso detectados. La interface de usuario muestra el nombre de los puntos de acceso detectados no autorizados, registrándolos con fecha y hora en un archivo diario. La comunicación entre los módulos de detección y el módulo supervisor se realiza usando transceptores ZigBee. El alcance entre los módulos de detección y la oficina de control fue 95 metros y el alcance de detección fue 50 metros

Design automation algorithms for advanced lithography

In circuit manufacturing, as the technology nodes keep shrinking, conventional 193 nm immersion lithography (193i) has reached its printability limit. To continue the scaling with Moore's law, different kinds of advanced lithography have been proposed, such as multiple patterning lithography (MPL), extreme ultraviolet (EUV), electron beam lithography (EBL) and directed self-assembly (DSA). While these new technologies create enormous opportunities, they also pose great design challenges due to their unique process characteristics and stringent constraints. In order to smoothly adopt these advanced lithography technologies in integrated circuit (IC) fabrication, effective electronic design automation (EDA) algorithms must be designed and integrated into computer-aided design (CAD) tools to address the underlying design constraints and help the circuit designer to better facilitate the lithography process. In this thesis, we focus on algorithmic design and efficient implementation of EDA algorithm for advanced lithography, including directed self-assembly (DSA) and self-aligned double patterning (SADP), to conquer the physical challenges and improve the manufacturing yield. The first advanced lithography technology we explore is self-aligned double patterning (SADP). SADP has the significant advantage over traditional litho-etch-litho-etch (LELE) double patterning in its ability to eliminate overlay, making it a preferable DPL choice for the 14 nm technology node. As in any DPL technology, layout decomposition is the key problem. While the layout decomposition problem for LELE DPL has been well studied in the literature, only a few attempts have been made for the SADP layout decomposition problem. This thesis studies the SADP decomposition problem in different scenarios. SADP has been successfully deployed in 1D patterns and has several applications; however, applying it to 2D patterns turns out to be much more difficult. All previous exact algorithms were based on computationally expensive methods such as SAT or ILP. Other previous algorithms were heuristics without a guarantee that an overlay-free solution can be found even if one exists. The SADP decomposition problem on general 2D layout is proven to be NP-complete. However, we show that if we restrict the overlay, the problem is polynomial-time solvable, and present an exact algorithm to determine if a given 2D layout has a no-overlay SADP decomposition. When designing the layout decomposition algorithms, it is usually useful to take the layout structure into consideration. As most of the current IC layouts adopt a row-based standard cell design style, we can take advantage of its characteristics and design more efficient algorithms compared to the algorithms for general 2D patterns. In particular, the fixed widths of standard cells and power tracks on top and bottom of cells suggest that improvements can be made over the algorithms for general decomposition problem. We present a shortest-path based polynomial time SADP decomposition algorithm for row-based standard cell layout that efficiently finds decompositions with minimum overlay violations. Our proposed algorithm takes advantage of the fixed width of the cells and the alternating power tracks between the rows to limit the possible decompositions and thus achieve high efficiency. The next advanced lithography technology we discuss in the thesis is directed self-assembly (DSA). Block copolymer directed self-assembly (DSA) is a promising technique for patterning contact holes and vias in 7 nm technology nodes. To pattern contacts/vias with DSA, guiding templates are usually printed first with conventional lithography (193i) that has a coarser pitch resolution. Contact holes are then patterned with DSA process. The guiding templates play the role of defining the DSA patterns, which have a finer resolution than the templates. As a result, different patterns can be obtained through controlling the templates. It is shown that DSA lithography is very promising in patterning contacts/vias in 7 nm technology node. However, to utilize DSA for full-chip manufacturing, EDA for DSA must be fully explored because EDA is the key enabler for manufacturing, and the EDA research for DSA is still lagging behind. To pattern the contact layer with DSA, we must ensure that all the contacts in the layout require only feasible DSA templates. Nevertheless, the original layout may not be designed in a DSA-friendly way. However, even with an optimized library, infeasible templates may be introduced after the physical design phase. We propose a simulated-annealing (SA) based scheme to perform full-chip level contact layer optimization. According to the experimental results, the DSA conflicts in the contact layer are reduced by close to 90% on average after applying the proposed optimization algorithm. It is a current trend that industry is transiting from the random 2D designs to highly regular 1D gridded designs for sub-20 nm nodes and fabricating circuit designs with print-cut technology. In this process, the randomly distributed cuts may be too dense to be printed by single patterning lithography. DSA has proven its success in contact hole patterning, and can be easily expanded to cut printing for 1D gridded designs. Nevertheless, the irregular distribution of cuts still presents a great challenge for DSA, as the self-assembly process usually forms regular patterns. As a result, the cut layer must be optimized for the DSA process. To address the above problem, we propose an efficient algorithm to optimize cut layers without hurting the original circuit logic. Our work utilizes a technique called `line-end extension' to move the cuts and extend the functional wires without changing the original functionality of the circuit. Consequently, the cuts can be redistributed and grouped into valid DSA templates. Multiple patterning lithography has been widely adopted for today's circuit manufacturing. However, increasing the number of masks will make the manufacturing process more expensive. By incorporating DSA into the multiple patterning process, it is possible to reduce the number of masks and achieve a cost-effective solution. We study the decomposition problem for the contact layer in row-based standard cell layout with DSA-MP complementary lithography. We explore several heuristic-based approaches, and propose an algorithm that decomposes a standard cell row optimally in polynomial-time. Our experiments show that our algorithm is guaranteed to find a minimum cost solution if one exists, while the heuristic cannot or only finds a sub-optimal solution. Our results show that the DSA-MP complementary approach is very promising for the future advanced nodes. As in any lithography technique, the process variation control and proximity correction are the most important issues. As the DSA templates are patterned by conventional lithography, the patterned templates are prone to deviate from mask shapes due to process variations, which will ultimately affect the contacts after the DSA process even for the same type of template. Therefore, in order to enable the DSA technology in contact/via layer printing, it is extremely important to accurately model and detect hotspots, as well as estimate the contact pitch and locations during the verification phase. We propose a machine learning based design automation framework for DSA verification. A novel DSA model and a set of features are included. We implemented the proposed ML-based flow and performed extensive experiments on comparing the performances of learning algorithms and features. The experimental results show that our approach is much more efficient than the traditional approach, and can produce highly accurate results

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Development of Supported 1D Nanomaterials by Vacuum and Plasma Technologies: From Sensors to Nanogenerators

Memoria presentada para optar al grado de Doctor por la Universidad de Sevilla Sevilla, September 2015Peer reviewe