A Fast TCAD-based Methodology for Variation Analysis of Emerging Nano-Devices

Variability analysis of nanoscale transistors and circuits is emerging as a necessity at advanced technology nodes. Technology Computer Aided Design (TCAD) tools are powerful ways to get an accurate insight of Process Variations (PV). However, obtaining both fast and accurate device simulations is impractical with current TCAD solvers. In this paper, we propose an automated output prediction method suited for fast PV analysis. Coupled with TCAD simulations, our methodology can substantially reduce the time complexity and cost of variation analysis for emerging technologies. We overcome the simulation obstacles and preserve accuracy, using a neural network based regression to predict the output of individual process simula- tions. Experiments indicate that, after the training process, the proposed methodology effectively accelerate TCAD-based PV simulations close to compact-model-based simulations. Therefore, the methodology can be an excellent opportunity in enabling extensive statistical simulations such as Monte-Carlo for emerging nano-devices

Implémentation de PCM (Process Compact Models) pour l’étude et l’amélioration de la variabilité des technologies CMOS FDSOI avancées

Recently, the race for miniaturization has seen its growth slow because of technological challenges it entails. These barriers include the increasing impact of the local variability and processes from the increasing complexity of the manufacturing process and miniaturization, in addition to the difficult of reducing the channel length. To address these challenges, new architectures, very different from the traditional one (bulk), have been proposed. However these new architectures require more effort to be industrialized. Increasing complexity and development time require larger financial investments. In fact there is a real need to improve the development and optimization of devices. This work gives some tips in order to achieve these goals. The idea to address the problem is to reduce the number of trials required to find the optimal manufacturing process. The optimal process is one that results in a device whose performance and dispersion reach the predefined aims. The idea developed in this thesis is to combine TCAD tool and compact models in order to build and calibrate what is called PCM (Process Compact Model). PCM is an analytical model that establishes linkages between process and electrical parameters of the MOSFET. It takes both the benefits of TCAD (since it connects directly to the process parameters electrical parameters) and compact (since the model is analytic and therefore faster to calculate). A sufficiently robust predictive and PCM can be used to optimize performance and overall variability of the transistor through an appropriate optimization algorithm. This approach is different from traditional development methods that rely heavily on scientific expertise and successive tests in order to improve the system. Indeed this approach provides a deterministic and robust mathematical framework to the problem. The concept was developed, tested and applied to transistors 28 and 14 nm FD-SOI and to TCAD simulations. The results are presented and recommendations to implement it at industrial scale are provided. Some perspectives and applications are likewise suggested.Récemment, la course à la miniaturisation a vue sa progression ralentir à cause des défis technologiques qu’elle implique. Parmi ces obstacles, on trouve l’impact croissant de la variabilité local et process émanant de la complexité croissante du processus de fabrication et de la miniaturisation, en plus de la difficulté à réduire la longueur du canal. Afin de relever ces défis, de nouvelles architectures, très différentes de celle traditionnelle (bulk), ont été proposées. Cependant ces nouvelles architectures demandent plus d’efforts pour être industrialisées. L’augmentation de la complexité et du temps de développement requièrent de plus gros investissements financier. De fait il existe un besoin réel d’améliorer le développement et l’optimisation des dispositifs. Ce travail donne quelques pistes dans le but d’atteindre ces objectifs. L’idée, pour répondre au problème, est de réduire le nombre d’essai nécessaire pour trouver le processus de fabrication optimal. Le processus optimal est celui qui conduit à un dispositif dont les performances et leur dispersion atteignent les objectifs prédéfinis. L’idée développée dans cette thèse est de combiner l’outil TCAD et les modèles compacts dans le but de construire et calibrer ce que l’on appelle un PCM (Process Compact Model). Un PCM est un modèle analytique qui établit les liens entre les paramètres process et électriques du MOSFET. Il tire à la fois les bénéfices de la TCAD (puisqu’il relie directement les paramètres process aux paramètres électriques) et du modèle compact (puisque le modèle est analytique et donc rapide à calculer). Un PCM suffisamment prédictif et robuste peut être utilisé pour optimiser les performances et la variabilité globale du transistor grâce à un algorithme d’optimisation approprié. Cette approche est différente des méthodes de développement classiques qui font largement appel à l’expertise scientifique et à des essais successifs dans le but d’améliorer le dispositif. En effet cette approche apporte un cadre mathématique déterministe et robuste au problème.Le concept a été développé, testé et appliqué aux transistors 28 et 14 nm FD-SOI ainsi qu’aux simulations TCAD. Les résultats sont exposés ainsi que les recommandations nécessaires pour implémenter la technique à échelle industrielle. Certaines perspectives et applications sont de même suggérées

Robustness Analysis of Controllable-Polarity Silicon Nanowire Devices and Circuits

Substantial downscaling of the feature size in current CMOS technology has confronted digital designers with serious challenges including short channel effect and high amount of leakage power. To address these problems, emerging nano-devices, e.g., Silicon NanoWire FET (SiNWFET), is being introduced by the research community. These devices keep on pursuing Mooreâs Law by improving channel electrostatic controllability, thereby reducing the Off âstate leakage current. In addition to these improvements, recent developments introduced devices with enhanced capabilities, such as Controllable-Polarity (CP) SiNWFETs, which make them very interesting for compact logic cell and arithmetic circuits. At advanced technology nodes, the amount of physical controls, during the fabrication process of nanometer devices, cannot be precisely determined because of technology fluctuations. Consequently, the structural parameters of fabricated circuits can be significantly different from their nominal values. Moreover, giving an a-priori conclusion on the variability of advanced technologies for emerging nanoscale devices, is a difficult task and novel estimation methodologies are required. This is a necessity to guarantee the performance and the reliability of future integrated circuits. Statistical analysis of process variation requires a great amount of numerical data for nanoscale devices. This introduces a serious challenge for variability analysis of emerging technologies due to the lack of fast simulation models. One the one hand, the development of accurate compact models entails numerous tests and costly measurements on fabricated devices. On the other hand, Technology Computer Aided Design (TCAD) simulations, that can provide precise information about devices behavior, are too slow to timely generate large enough data set. In this research, a fast methodology for generating data set for variability analysis is introduced. This methodology combines the TCAD simulations with a learning algorithm to alleviate the time complexity of data set generation. Another formidable challenge for variability analysis of the large circuits is growing number of process variation sources. Utilizing parameterized models is becoming a necessity for chip design and verification. However, the high dimensionality of parameter space imposes a serious problem. Unfortunately, the available dimensionality reduction techniques cannot be employed for three main reasons of lack of accuracy, distribution dependency of the data points, and finally incompatibility with device and circuit simulators. We propose a novel technique of parameter selection for modeling process and performance variation. The proposed technique efficiently addresses the aforementioned problems. Appropriate testing, to capture manufacturing defects, plays an important role on the quality of integrated circuits. Compared to conventional CMOS, emerging nano-devices such as CP-SiNWFETs have different fabrication process steps. In this case, current fault models must be extended for defect detection. In this research, we extracted the possible fabrication defects, and then proposed a fault model for this technology. We also provided a couple of test methods for detecting the manufacturing defects in various types of CP-SiNWFET logic gates. Finally, we used the obtained fault model to build fault tolerant arithmetic circuits with a bunch of superior properties compared to their competitors

Similarity Learning Over Large Collaborative Networks

In this thesis, we propose novel solutions to similarity learning problems on collaborative networks. Similarity learning is essential for modeling and predicting the evolution of collaborative networks. In addition, similarity learning is used to perform ranking, which is the main component of recommender systems. Due to the the low cost of developing such collaborative networks, they grow very quickly, and therefore, our objective is to develop models that scale well to large networks. The similarity measures proposed in this thesis make use of the global link structure of the network and of the attributes of the nodes in a complementary way. We first define a random walk model, named Visiting Probability (VP), to measure proximity between two nodes in a graph. VP considers all the paths between two nodes collectively and thus reduces the effect of potentially unreliable individual links. Moreover, using VP and the structural characteristics of small-world networks (a frequent type of networks), we design scalable algorithms based on VP similarity. We then model the link structure of a graph within a similarity learning framework, in which the transformation of nodes to a latent space is trained using a discriminative model. When trained over VP scores, the model is able to better predict the relations in a graph in comparison to models learned directly from the network’s links. Using the VP approach, we explain how to transfer knowledge from a hypertext encyclopedia to text analysis tasks. We consider the graph of Wikipedia articles with two types of links between them: hyperlinks and content similarity ones. To transfer the knowledge learned from the Wikipedia network to text analysis tasks, we propose and test two shared representation methods. In the first one, a given text is mapped to the corresponding concepts in the network. Then, to compute similarity between two texts, VP similarity is applied to compute the distance between the two sets of nodes. The second method uses the latent space model for representation, by training a transformation from words to the latent space over VP scores. We test our proposals on several benchmark tasks: word similarity, document similarity / clustering / classification, information retrieval, and learning to rank. The results are most often competitive compared to state-of-the-art task-specific methods, thus demonstrating the generality of our proposal. These results also support the hypothesis that both types of links over Wikipedia are useful, as the improvement is higher when both are used. In many collaborative networks, different link types can be used in a complementary way. Therefore, we propose two joint similarity learning models over the nodes’ attributes, to be used for link prediction in networks with multiple link types. The first model learns a similarity metric that consists of two parts: the general part, which is shared between all link types, and the specific part, which is trained specifically for each type of link. The second model consists of two layers: the first layer, which is shared between all link types, embeds the objects of the network into a new space, and then a similarity is learned specifically for each link type in this new space. Our experiments show that the proposed joint modeling and training frameworks improve link prediction performance significantly for each link type in comparison to multiple baselines. The two-layer similarity model outperforms the first one, as expected, due to its capability of modeling negative correlations among different link types. Finally, we propose a learning to rank algorithm on network data, which uses both the attributes of the nodes and the structure of the links for learning and inference. Link structure is used in training through a neighbor-aware ranker which considers both node attributes and scores of neighbor nodes. The global link structure of the network is used in inference through an original propagation method called the Iterative Ranking Algorithm. This propagates the predicted scores in the graph on condition that they are above a given threshold. Thresholding improves performance, and makes a time-efficient implementation possible, for application to large scale graphs. The observed improvements are explained considering the structural properties of small-world networks

Polarity Control at Runtime:from Circuit Concept to Device Fabrication

Semiconductor device research for digital circuit design is currently facing increasing challenges to enhance miniaturization and performance. A huge economic push and the interest in novel applications are stimulating the development of new pathways to overcome physical limitations affecting conventional CMOS technology. Here, we propose a novel Schottky barrier device concept based on electrostatic polarity control. Specifically, this device can behave as p- or n-type by simply changing an electric input bias. This device combines More-than-Moore and Beyond CMOS elements to create an efficient technology with a viable path to Very Large Scale Integration (VLSI). This thesis proposes a device/circuit/architecture co-optimization methodology, where aspects of device technology to logic circuit and system design are considered. At device level, a full CMOS compatible fabrication process is presented. In particular, devices are demonstrated using vertically stacked, top-down fabricated silicon nanowires with gate-all-around electrode geometry. Source and drain contacts are implemented using nickel silicide to provide quasi-symmetric conduction of either electrons or holes, depending on the mode of operation. Electrical measurements confirm excellent performance, showing Ion/Ioff > 10^7 and subthreshold slopes approaching the thermal limit, SS ~ 60mV/dec (~ 63mV/dec) for n(p)-type operation in the same physical device. Moreover, the shown devices behave as p-type for a polarization bias (polarity gate voltage, Vpg) of 0V, and n-type for a Vpg = 1V, confirming their compatibility with multi-level static logic circuit design. At logic gate level, two- and four-transistor logic gates are fabricated and tested. In particular, the first fully functional, two-transistor XOR logic gate is demonstrated through electrical characterization, confirming that polarity control can enable more compact logic gate design with respect to conventional CMOS. Furthermore, we show for the first time fabricated four- transistors logic gates that can be reconfigured as NAND or XOR only depending on their external connectivity. In this case, logic gates with full swing output range are experimentally demonstrated. Finally, single device and mixed-mode TCAD simulation results show that lower Vth and more optimized polarization ranges can be expected in scaled devices implementing strain or high-k technologies. At circuit and system level, a full semi-custom logic circuit design tool flow was defined and configured. Using this flow, novel logic libraries based on standard cells or regular gate fabrics were compared with standard CMOS. In this respect, results were shown in comparison to CMOS, including a 40% normalized area-delay product reduction for the analyzed standard cell libraries, and improvements of over 2× in terms of normalized delay for regular Controlled Polarity (CP)-based cells in the context of Structured ASICs. These results, in turn, confirm the interest in further developing and optimizing CP devices, as promising candidates for future digital circuit technology