Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Two-step sinter-crystallization of K2O–CaO–P2O5–SiO2 (45S5-K) bioactive glass

Bioactive glasses and glass-ceramics (GCs) effectively regenerate bone tissue, however most GCs show improved mechanical properties. In this work, we developed and tested a rarely studied bioactive glass composition (24.4K₂O-26.9CaO-46.1SiO₂-2.6P₂O₅ mol%, identified as 45S5-K) with different particle sizes and heating rates to obtain a sintered GC that combines good fracture strength, low elastic modulus, and bioactivity. We analyzed the influence of the sintering processing conditions in the elastic modulus, Vickers microhardness, density, and crystal phase formation in the GC. The best GC shows improved properties compared with its parent glass. This glass achieves a good densification degree with a two-step viscous flow sintering approach and the resulting GC shows as high bioactivity as that of the standard 45S5 Bioglass®. Furthermore, the GC elastic modulus (56 GPa) is relatively low, minimizing stress shielding. Therefore, we unveiled the glass sintering behavior with concurrent crystallization of this complex bioactive glass composition and developed a potential GC for bone regeneration.     

Fault-aware Communication Mapping for NoCs with Guaranteed Latency

As feature sizes shrink, transient failures of on-chip network links become a critical problem. At the same time, many applications require guarantees on both message arrival probability and response time. We address the problem of transient link failures by means of temporally and spatially redundant transmission of messages, such that designer-imposed message arrival probabilities are guaranteed. Response time minimisation is achieved by a heuristic that statically assigns multiple copies of each message to network links, intelligently combining temporal and spatial redundancy. Concerns regarding energy consumption are addressed in two ways. First, we reduce the total amount of transmitted messages, and, second, we minimise the application response time such that the resulted time slack can be exploited for energy savings through voltage reduction. The advantages of the proposed approach are guaranteed message arrival probability and guaranteed worst case application response time.     

Effect of beam voltage on the properties of aluminium nitride prepared by ion beam assisted deposition

The effects of nitrogen-beam voltage on the structure, stress, energy band gap and hardness of AIN thin films deposited on Si (111), Si (100) and sapphire (0001) by ion beam assisted deposition (IBAD) are reported. As the nitrogen-beam voltage was increased from 50 to 200 V, the stress and disorder in the AIN films increased as determined by X-ray diffraction, FTIR and Raman spectroscopy. The preferred orientation of the film's c-axis changed from completely normal to the film at 100 V, to a mixture of normal and in the plane of the film at 200 V. For AIN films deposited under the same conditions, the films were more highly oriented on sapphire (0001) than in Si (111). The hardness of the films increased from 18.2 to 23.7 GPa with the nitrogen-beam voltage, and possible reasons for this change in hardness are considered.     

Modeling Solvent Drying of Coated Webs Including the Initial Transient

A complete model for the drying of coated webs. as implemented in the spreadsheet DRYWEB for Lotus and for Excel, is described. The model predicts the temperatures and solvent levels throughout the length of the dryer. The initial transient as the coated web approaches the equilibrium temperature in the consiant rate period is calculated. The coating is divided into slices for a finite difference calculation of solvent diffusion to the surface, using a simple free volume model of diffusion. The semi-empirical constants needed can be found from one or two trial runs. The possible use of infra-red heating in addition to convection heating is included in both the constant and falling rates. The calculation of infra-red energy is discussed. The model assumes that the temperature is uniform from the top of the coating to the bottom of the base, and that assumption is justified.     

Synthesizing Energy-Efficient Embedded Systems with LOPOCOS

In this paper, we introduce the LOPOCOS (Low Power Co-synthesis) system, a prototype CAD tool for system level co-design. LOPOCOS targets the design of energy-efficient embedded systems implemented as heterogeneous distributed architectures. In particular, it is designed to solve the specific problems involved in architectures that include dynamic voltage scalable (DVS) processors. The aim of this paper is to demonstrate how LOPOCOS can support the system designer in identifying energy-efficient hardware/software implementations for the desired embedded systems. Hence, highlighting the necessary optimization steps during design space exploration for DVS enable architectures. The optimization steps carried out in LOPOCOS involve component allocation and task/communication mapping as well as scheduling and dynamic voltage scaling. LOPOCOS has the following key features, which contribute to this energy efficiency. During the voltage scaling valuable power profile information of task execution is taken into account, hence, the accuracy of the energy estimation is improved. A combined optimization for scheduling and communication mapping based on genetic algorithm, optimizes simultaneously execution order and communication mapping towards the utilization of the DVS processors and timing behaviour. Furthermore, a separation of task and communication mapping allows a more effective implementation of both task and communication mapping optimizationsteps. Extensive experiments are conducted to demonstrate the efficiency of LOPOCOS. We report up to 38% higher energy reductions compared to previous co-synthesis techniques for DVS systems. The investigations include a real-life example of an optical flow detection algorithm.