Physics-Based Modeling of Phase-Change Memory Devices and Materials

Artificial intelligence (AI) has become a key enabler in many applications but requires fast and energy-efficient computation. IBM is exploring an “Analog AI acceleration approach using phase change memories (PCM). As such, there is a need to accurately model the physics inside each PCM cell, including the interplay between thermal and electrical dynamics, and the impact of PCM polycrystal grains and grain boundaries in materials such as Ge2Sb2Te5. The thesis uses an existing thermo electrodynamic Technology Computer-Aided Design (TCAD), which was created at IBM, to simulate the intermediate resistance states critical to the “Analog AI application. The thesis discusses the simulations thermal behavior, simulates against industrial tools, and uses the three-dimensional conservation of energy to confirm the requirement of a TCAD tool. As an accurate thermal simulator, the thesis continues to discuss and implement Poole Frenkel to simulate against experimental PCM device data. Thereafter, it discusses the complication of chalcogenide material and the impact of the polycrystalline electrical impact of grain boundaries. The availability of an accurate TCAD simulation is expected to have significant impact on the understanding and design of future Analog AI systems

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

A novel representation of energy and signal transformation in measurement systems

This work presents a novel representation of energy and signal transformation in a measurement system, which is essentially a transducer conversion logic or language (TCL). Using two-port and three-port transducers as basic building blocks, it can be utilized to model any measurement system. It has the key features of object-orientation and consists of only text with very simple syntax. The TCL can be easily handled and processed by computers. This paper has demonstrated its use in description, classification, and computer-aided analysis and design of measuring instruments with some preliminary test results. It will find wide applications in modeling, analysis, design, and education in measurement, control, and information processing

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Physics-based large-signal sensitivity analysis of microwave circuits using technological parametric sensitivity from multidimensional semiconductor device models

The authors present an efficient approach to evaluate the large-signal (LS) parametric sensitivity of active semiconductor devices under quasi-periodic operation through accurate, multidimensional physics-based models. The proposed technique exploits efficient intermediate mathematical models to perform the link between physics-based analysis and circuit-oriented simulations, and only requires the evaluation of dc and ac small-signal (dc charge) sensitivities under general quasi-static conditions. To illustrate the technique, the authors discuss examples of sensitivity evaluation, statistical analysis, and doping profile optimization of an implanted MESFET to minimize intermodulation which makes use of LS parametric sensitivities under two-tone excitatio