Compact model of retention characteristics of ferroelectric FinFET synapse with MFIS gate stack

In this paper, multiple-fin n- and p-channel HfZrO2 ferroelectric-FinFET devices are manufactured using a gate first process with post metalization annealing. The device transfer characteristics upon program and erase operations are measured and modeled. The drift in the transfer characteristics due to depolarization field and charge injection are captured using the shift in the threshold voltage along with time-dependent modeling of vertical field dependent mobility degradation parameters to develop a physical, computationally efficient, and accurate retention model for ferroelectric-FinFET devices. The modeled conductance is incorporated into deep neural network simulation platform CIMulator to analyze the role of conductance drift due to retention degradation, as well as the importance of the gap between high and low conductance states in improving the image recognition accuracy of neural networks.

Improving Student Retention by Providing Targeted Support to University Students who do not Submit an Early Assessment Item

The Charles Sturt University Retention Team has developed, tested, evaluated, and refined a retention model through 14 action-research cycles from 2017-2021. The project has expanded from a small pilot in one faculty to monitoring the engagement and submission of an early assessment item for over 70% of all commencing undergraduate students across the University. The Retention Model synergistically overlays curriculum design and student support and ensures academics embed best practice transition pedagogy and learning engagement activities into key first-year subjects. By monitoring the submission of early assessment items, the team can accurately identify and proactively contact students who are not engaged in their studies prior to their first census date. Every aspect of this program supports equity student groups that are over-represented at our regional university. This work has significantly improved commencing progress rates across the institution.

Evaluating different machine learning methods to simulate runoff from extensive green roofs

Green roofs are increasingly popular measures to permanently reduce or delay storm-water runoff. The main objective of the study was to examine the potential of using machine learning (ML) to simulate runoff from green roofs to estimate their hydrological performance. Four machine learning methods, artificial neural network (ANN), M5 model tree, long short-term memory (LSTM) and k nearest neighbour (kNN), were applied to simulate storm-water runoff from 16 extensive green roofs located in four Norwegian cities across different climatic zones. The potential of these ML methods for estimating green roof retention was assessed by comparing their simulations with a proven conceptual retention model. Furthermore, the transferability of ML models between the different green roofs in the study was tested to investigate the potential of using ML models as a tool for planning and design purposes. The ML models yielded low volumetric errors that were comparable with the conceptual retention models, which indicates good performance in estimating annual retention. The ML models yielded satisfactory modelling results (NSE >0.5) in most of the roofs, which indicates an ability to estimate green roof detention. The variations in ML models' performance between the cities was larger than between the different configurations, which was attributed to the different climatic characteristics between the four cities. Transferred ML models between cities with similar rainfall events characteristics (Bergen–Sandnes, Trondheim–Oslo) could yield satisfactory modelling performance (Nash–Sutcliffe efficiency NSE >0.5 and percentage bias |PBIAS| <25 %) in most cases. However, we recommend the use of the conceptual retention model over the transferred ML models, to estimate the retention of new green roofs, as it gives more accurate volume estimates. Follow-up studies are needed to explore the potential of ML models in estimating detention from higher temporal resolution datasets.

Coupling cyclic and water retention response of a clayey sand subjected to traffic and environmental cycles

Compacted soils used as formation layers of railways and roads continuously undergo water content and suction changes due to seasonal variations. Such variations together with the impact of cyclic traffic-induced loads can alter the hydro-mechanical behaviour of the soil, which in turn affects the performance of the superstructure. This study investigates the impact of hydraulic cycles on the coupled water retention and cyclic response of a compacted soil. Suction-monitored cyclic triaxial tests were performed on a compacted clayey sand. The cyclic response of the soil obtained after applying drying and wetting paths was different to that obtained immediately after compaction. The results showed that both suction and degree of saturation are required to interpret the cyclic behaviour. A new approach was developed using (i) a hysteretic water retention model to predict suction variations during cyclic loading and (ii) Bishop's stress together with a bonding parameter to predict accumulated permanent strain and resilient modulus. The proposed formulations were able to predict the water retention behaviour, accumulated permanent strains and resilient modulus well, indicating the potential capability of using the fundamentals of unsaturated soils for predicting the effects of drying and wetting cycles on the coupled soil water retention and cyclic response.

Human Resources Remain Our Biggest Assets

This chapter analyses employees as human assets by investigating various retention theories. It is imperative that employers do not treat employees like “cogs in the wheel” but rather understand what factors would retain these individuals. The working environment in any organisation is important, as it has to be conducive to attaining a competent and successful workforce. The chapter focuses on a research study conducted evaluating what makes employees remain in an organisation. From the findings a conceptual retention model was developed which would assist employers in retaining staff and ensuring they are treated as human assets. The retention model was based on both a quantitative and qualitative analysis, and many themes and theories have been included in this model.

High Pressure Deuterium Passivation of Charge Trapping Layer for Nonvolatile Memory Applications

In this study, the deuterium passivation effect of silicon nitride (Si3N4) on data retention characteristics is investigated in a Metal-Nitride-Oxide-Silicon (MNOS) memory device. To focus on trap passivation in Si3N4 as a charge trapping layer, deuterium (D2) high pressure annealing (HPA) was applied after Si3N4 deposition. Flat band voltage shifts (ΔVFB) in data retention mode were compared by CV measurement after D2 HPA, which shows that the memory window decreases but charge loss in retention mode after program is suppressed. Trap energy distribution based on thermal activated retention model is extracted to compare the trap density of Si3N4. D2 HPA reduces the amount of trap densities in the band gap range of 1.06–1.18 eV. SIMS profiles are used to analyze the D2 profile in Si3N4. The results show that deuterium diffuses into the Si3N4 and exists up to the Si3N4-SiO2 interface region during post-annealing process, which seems to lower the trap density and improve the memory reliability.

A Simple Water Retention Model Based on Grain Size Distribution

The Hunter valley region in NSW Australia is an area with a heavy coal mining presence. As some mines come to their end of life, options are being investigated to improve the topsoil on post mining land for greater plant growth, which may allow economically beneficial farmland to be created. This research is part of an investigation into mixing a mine waste material, coal tailings, with topsoil in order to produce an improved soil for plant growth. Implementing such a solution requires estimation of the drying path of the water retention curves for the tailings and topsoil used. Instead of a lengthy laboratory measurement, a prediction of the drying curve is convenient in this context. No existing prediction models were found that were suitable for these mine materials, hence this paper proposes a simple and efficient model that can more accurately predict drying curves for these mine materials. The drying curves of two topsoils and two tailings from Australian coal mines were measured and compared with predictions using the proposed model, which performs favorably compared to several existing models in the literature. Additionally, the proposed model is assessed using data from a variety of fine- and coarse-grained materials in the literature. It is shown that the proposed model is overall more accurate than every other model assessed, indicating the model may be useful for various materials other than those considered in this study.