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Register a new user3D logic cells design and results based on Vertical NWFET technology including tied compact model
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Gate-all-around Vertical Nanowire Field Effect Transistors (VNWFET) are emerging devices, which are well suited to pursue scaling beyond lateral scaling limitations around 7nm. This work explores the relative merits and drawbacks of the technology in the context of logic cell design. We describe a junctionless nanowire technology and associated compact model, which accurately describes fabricated device behavior in all regions of operations for transistors based on between 16 and 625 parallel nanowires of diameters between 22 and 50nm. We used this model to simulate the projected performance of inverter logic gates based on passive load, active load and complementary topologies and carry out an performance exploration for the number of nanowires in transistors. In terms of compactness, through a dedicated full 3D layout design, we also demonstrate a 1.4x reduction in lateral dimensions for the complementary structure with respect to 7nm FinFET-based inverters.
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Model-based Systems Engineering (MBSE) has been widely utilized to formalize system artifacts and facilitate their development throughout the entire lifecycle. During complex system development, MBSE models need to be frequently exchanged across stakeholders. Concerns about data security and tampering using traditional data exchange approaches obstruct the construction of a reliable marketplace for digital assets. The emerging Distributed Ledger Technology (DLT), represented by blockchain, provides a novel solution for this purpose owing to its unique advantages such as tamper-resistant and decentralization. In this paper, we integrate MBSE approaches with DLT aiming to create a decentralized marketplace to facilitate the exchange of digital engineering assets (DEAs). We first define DEAs from perspectives of digital engineering objects, development processes and system architectures. Based on this definition, the Graph-Object-Property-Point-Role-Relationship (GOPPRR) approach is used to formalize the DEAs. Then we propose a framework of a decentralized DEAs marketplace and specify the requirements, based on which we select a Directed Acyclic Graph (DAG) structured DLT solution. As a proof-of-concept, a prototype of the proposed DEAs marketplace is developed and a case study is conducted to verify its feasibility. The experiment results demonstrate that the proposed marketplace facilitates free DEAs exchange with a high level of security, efficiency and decentralization.
Businesses, particularly small and medium-sized enterprises, aiming to start up in Model-Based Design (MBD) face difficult choices from a wide range of methods, notations and tools before making the significant investments in planning, procurement and training necessary to deploy new approaches successfully. In the development of Cyber-Physical Systems (CPSs) this is exacerbated by the diversity of formalisms covering computation, physical and human processes. In this paper, we propose the use of a cloud-enabled and open collaboration platform that allows businesses to offer models, tools and other assets, and permits others to access these on a pay-per-use basis as a means of lowering barriers to the adoption of MBD technology, and to promote experimentation in a sandbox environment.
We propose a policy search approach to learn controllers from specifications given as Signal Temporal Logic (STL) formulae. The system model is unknown, and it is learned together with the control policy. The model is implemented as a feedforward neural network (FNN). To capture the history dependency of the STL specification, we use a recurrent neural network (RNN) to implement the control policy. In contrast to prevalent model-free methods, the learning approach proposed here takes advantage of the learned model and is more efficient. We use control barrier functions (CBFs) with the learned model to improve the safety of the system. We validate our algorithm via simulations. The results show that our approach can satisfy the given specification within very few system runs, and therefore it has the potential to be used for on-line control.
We consider the computation of resilient controllers for perturbed non-linear dynamical systems w.r.t. linear-time temporal logic specifications. We address this problem through the paradigm of Abstraction-Based Controller Design (ABCD) where a finite state abstraction of the perturbed system dynamics is constructed and utilized for controller synthesis. In this context, our contribution is twofold: (I) We construct abstractions which model the impact of occasional high disturbance spikes on the system via so called disturbance edges. (II) We show that the application of resilient reactive synthesis techniques to these abstract models results in closed loop systems which are optimally resilient to these occasional high disturbance spikes. We have implemented this resilient ABCD workflow on top of SCOTS and showcase our method through multiple robot planning examples.
Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via machine learning (ML). In multistage fractured completions, post-fracturing production analysis reveals that different stages produce very non-uniformly due to a combination of geomechanics and fracturing design factors. Hence, there is a significant room for improvement of current design practices. The workflow is essentially split into two stages. As a result of the first stage, the present paper summarizes the efforts into the creation of a digital database of field data from several thousands of multistage HF jobs on wells from circa 20 different oilfields in Western Siberia, Russia. In terms of the number of points (fracturing jobs), the present database is a rare case of a representative dataset of about 5000 data points. Each point in the data base contains the vector of 92 input variables (the reservoir, well and the frac design parameters) and the vector of production data, which is characterized by 16 parameters, including the target, cumulative oil production. Data preparation has been done using various ML techniques: the problem of missing values in the database is solved with collaborative filtering for data imputation; outliers are removed using visualisation of cluster data structure by t-SNE algorithm. The production forecast problem is solved via CatBoost algorithm. Prediction capability of the model is measured with the coefficient of determination (R^2) and reached 0.815. The inverse problem (selecting an optimum set of fracturing design parameters to maximize production) will be considered in the second part of the study to be published in another paper, along with a recommendation system for advising DESC and production stimulation engineers on an optimized fracturing design.
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