Integrating Energy Efficiency Research Articles

Methodology of Multiple-Criteria Decision Making for Selecting a Refrigerant to Be Used in Commercial Refrigeration Equipment

This paper presents the application of the multiple-criteria decision-making (SAW) method for selecting the optimum refrigerant for the refrigeration systems of commercial cooling equipment used in gastronomy furniture, which is paramount in storing food under optimal conditions. The analysis focused on comparing different refrigerants, including natural refrigerants such as R744 (carbon dioxide) and R290 (propane) and synthetic refrigerants such as R455A, R449A, and R452A. As a result of the analysis using the SAW method, the refrigerant R455A was found to be the best solution. This choice resulted from the consideration of various decision criteria, such as energy efficiency, environmental impact, operating costs, and technology availability. R455A stands out as a synthetic refrigerant that provides high energy efficiency with minimal environmental impact. Its use supports sustainability goals by reducing greenhouse gas emissions and electricity consumption, which is crucial given the modern regulatory requirements and environmental standards. This study offers a practical decision-making tool for commercial refrigeration equipment designers and manufacturers, supporting them while selecting the optimal technological solutions. The choice of refrigerant R455A addresses the need to integrate energy efficiency, environmental protection, and cost-effectiveness in the process of designing modern refrigeration systems for catering furniture.

Design and Implementation of Energy-Aware Integration Mechanisms for IoT-Cloud Architectures: Enhancing Efficiency in Resource-Constrained Environments

This study paper suggests a way for the Internet of Things (IoT) and Cloud computing to work together that is smart about energy use. The goal is to solve the problem of IoT-Cloud integration's energy efficiency, which is important for making sensor nodes last longer and making sure that data transfer continues over time. The first step in the process is to plan the IoT nodes and give each sensor node a unique ID and a starting energy level. To make data safer, a homomorphic encryption method is used to make public and private keys. The integration process uses a good Cluster Head Selection Technique that has Random, Energy-Based, Distance-Based, and Density-Based methods to make sure that the sensor nodes are grouped together in the best way possible. The sensor nodes send data to the base station through cluster heads, which use homomorphic encryption to make the connection safe. The public key is used to hash each data packet before it is sent to the cluster head. The cluster head then sends the packets to the base station through the Cloud. At the base station, the data hash is checked one at a time, and then the data is decrypted. This makes sure that the data is correct and safe while it is being sent. Comparing time and energy use across different systems and node numbers is part of the performance assessment. The results show that System 4 did better than the others in terms of how much energy it used and how efficiently it transferred data. This shows that the proposed system can effectively handle IoT-Cloud integration. System 4 kept 28,848 units of energy after starting with 29,442 units, showing that it was much more energy efficient. The results show that using techniques that are aware of energy in IoT-Cloud systems can greatly improve performance. This study adds to the growing body of research on integrating IoT and the cloud by showing a safe, effective, and energy-conscious method that sends data while using the least amount of energy possible.

Cross‐laminated timber for seismic retrofitting of RC buildings: Substructured pseudodynamic tests on a full‐scale prototype

AbstractThis paper presents an experimental study on an innovative timber‐based retrofit solution for reinforced concrete (RC) framed buildings, with or without masonry infills. The intervention aims to enhance seismic resistance through a light, cost‐effective, sustainable, and reversible approach integrating energy efficiency upgrades. The method employs cross‐laminated timber (CLT) panels as infills or external retrofitting elements, mechanically connected to the RC frame through steel fasteners. The system is combined with thermal insulation for improved energy efficiency. The seismic performance of the proposed retrofit technique was assessed experimentally on a full‐scale building model at the European Laboratory for Structural Assessment (ELSA). The experiments included tests on two five‐story building configurations: a masonry‐infilled RC building as a reference and the same structure strengthened with CLT panels. Each building was subjected to unidirectional earthquake simulations of increasing intensity using the pseudodynamic (PsD) testing method with substructuring. The physical substructure of the hybrid model consisted of the first story of a two‐story mockup built and retrofitted in the laboratory, while stories two to five were simulated numerically. The paper discusses major observations from the tests, comparing the damage evolution and hysteretic responses of the two configurations. The experiments yielded promising results, showing that the suggested retrofit solution significantly increased displacement and energy dissipation capacity. The retrofitted building survived earthquake intensities up to 50% higher than the non‐retrofitted counterpart, exhibiting only slight structural damage. These pioneering experiments provide compelling data on the high effectiveness of the proposed CLT‐based retrofit system in enhancing the seismic performance of full‐scale RC buildings.

A high efficiency rooftop air conditioning system using multi-speed compressors

This study delineates a meticulous exploration of technologies to enhance the energy efficiency of rooftop air conditioning units, employing the DOE/ORNL heat pump design model for comprehensive engineering design and optimization. A baseline rooftop air conditioning unit, featuring a 13 ton (45.7 kW) cooling capacity and a 17.9 integrated energy efficiency ratio, served as the point of departure for substantive efficiency enhancements. Key modifications included the consolidation of two refrigerant circuits into one, integrating three parallel 2-stage (dual-speed) compressors, fan replacements with high-efficiency substitutes. Notably, a lower global warming potential refrigerant, R452B, was evaluated as a substitute for R-410A, demonstrating better performance in the lab prototype. The achieved measured integrated energy efficiency ratio of 21.4 in the lab prototype surpassed the baseline integrated energy efficiency ratio. Comparative evaluations between R410A and R452B indicated heightened efficiency with the latter, showcasing a lab-demonstrated integrated energy efficiency ratio of 22.4 at the rated capacity of 13.8 ton (48.5 kW) and 23.9 integrated energy efficiency ratio at the rated capacity of 10 ton (35.2 kW). This research underscores the successful development of a rigorous, energy efficient rooftop air conditioning unit prototype with noteworthy environmental and economic implications.

High-Performance Method and Architecture for Attention Computation in DNN Inference.

In recent years, The combination of Attention mechanism and deep learning has a wide range of applications in the field of medical imaging. However, due to its complex computational processes, existing hardware architectures have high resource consumption or low accuracy, and deploying them efficiently to DNN accelerators is a challenge. This paper proposes an online-programmable Attention hardware architecture based on compute-in-memory (CIM) marco, which reduces the complexity of Attention in hardware and improves integration density, energy efficiency, and calculation accuracy. First, the Attention computation process is decomposed into multiple cascaded combinatorial matrix operations to reduce the complexity of its implementation on the hardware side; second, in order to reduce the influence of the non-ideal characteristics of the hardware, an online-programmable CIM architecture is designed to improve calculation accuracy by dynamically adjusting the weights; and lastly, it is verified that the proposed Attention hardware architecture can be applied for the inference of deep neural networks through Spice simulation. Based on the 100nm CMOS process, compared with the traditional Attention hardware architectures, the integrated density and energy efficiency are increased by at least 91.38 times, and latency and computing efficiency are improved by about 12.5 times.

Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing

Five-axis machining, especially flank milling, is popular in machining thin-walled freeform surface parts with high energy consumption. Reducing the machining energy consumption is paramount for advancing green manufacturing. Therefore, this paper proposes an energy-efficient integration optimisation of cutting parameters and tool path with hierarchical reinforcement learning (HRL). Firstly, a novel multi-pass machining energy consumption model is developed with cutting and path parameters, based on which the integrated optimisation problem is modelled considering a dynamic workpiece deformation constraint. Secondly, HRL with a Soft Actor Critic agent (HSAC) decouples the model into two Markov Decision Processes at different timescales. The higher-layer plans cutting parameters for each pass on a macro timescale, while the micro-timescale lower-layer performs multiple tool path expansions with the planned cutting parameters, and provides feedback to the higher layer. By hierarchical optimisation and non-hierarchical interaction, the model is efficiently solved. Moreover, curriculum transfer learning is applied to expedite task completion of the lower layer, enhancing interaction efficiency between the two layers. Experiments show that, compared with two benchmarks, the proposed method improves machining energy consumption by 35.02 % and 30.92 %, and reduces machining time by 38.57 % and 27.17 %, providing a promising paradigm of green practices for thin-walled freeform parts and the broader manufacturing industry.

SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region

The major problems in the power system network of the South Asian Association for Regional Cooperation (SAARC) region include the utilization of substantial fossil fuels, which leads to significant emissions of CO2. Moreover, due to the depletion of fossil fuels, it is very difficult to manage the demand requirement of the SAARC regions, which leads to significant power shortage issues. These problems would be resolved through the integration of the substantial amount of renewable energy resources (RERs) that are available in excess in the SAARC regions into the power system networks. This will provide a futuristic roadmap to a sustainable and green energy environment in the SAARC region. In this regard, the SAARC region has devised a new plan to transform its smart grid infrastructure into the super smart grid (SSG), which would connect the SAARC countries using the high voltage transmission lines utilizing each country's substantial RERs. To accelerate this ecofriendly effort, the key objective of this review study is to evaluate the potential of the SAARC SSG for demand management and emissions reduction. This study provides a comprehensive overview of SSG technology, covering its technical background, management, power flow improvement techniques, power stability techniques, simulation models, and future steps for development in the SAARC region. First, the regional conflicts, energy deficiency, and potential of the SAARC region have been discussed. Second, a potential architecture of the SAARC SSG has been simulated. Third, a novel Markov chain modeling has been presented for demand management in the SAARC regions. The results and discussions section presents findings from simulations and models. The paper also discusses CO2 reduction techniques, including methods for capturing, storing, and utilizing carbon in biofuel. The study also discusses futuristic optimization techniques in SSG, including blockchain, federated learning, reinforcement learning, the metaverse, digital twin technology, artificial intelligence, and cloud computing. The SSG challenges and limitations section summarizes the challenges and limitations faced by SSG. Finally, a roadmap to a sustainable and green energy environment for the SAARC power grids has been identified to support the implementation of the futuristic SAARC SSG. The results exhibited that there is a good potential for successful demand management and emissions reduction through the SAARC SSG.

Design and control studies of six-phase interleaved boost converter for integrated energy efficiency improvement of green ship

The operation of offshore vessels is characterized by stable loads during normal voyages and significant load variations when leaving port or docking. Conventional ships emit large amounts of greenhouse gases during navigation. In the development of the modern shipbuilding industry, how to effectively reduce gas emissions during ship sailing is a challenging task that still remains under-explored. Green ships equipped with new energy power systems have a greater potential for application. They are equipped with fuel cells and lithium batteries as energy sources, and such a hybrid system requires a comprehensive energy management system to achieve the power distribution between the two energy sources. In this paper, a powerful 10kW six-phase interleaved boost converter (IBC) for the hybrid power generation system of ships is designed to manage the output power of the fuel cell. Firstly, the new circuit topology design principle and construction process are introduced. Later, a step-by-step current adjustment controller is designed for the converter to progressively enforce output current and a maximum limitation of the fuel cell. Afterwards, LTspice simulation is performed to verify the ripple suppression effect and stable anti-interference performance. Finally, the designed IBC has been experimentally constructed, including the input/output current and voltage sampling, temperature control and sampling, and other functions, significantly benefiting real-world marine high-power fuel cells. The stability, reasonableness and effectiveness of the designed IBC are further evaluated through the extensive experimental analyses.

Exploring energy management integration into upstream supply chains: a systematic literature review

In the effort to limit global warming to 1.5°C above the pre-industrial level, decarbonization of supply chains is crucial. Companies will need to reduce not only their direct and indirect emissions (scope 1 and 2) but also their indirect emissions (scope 3). In some sectors, the upstream scope 3 emissions can actually be larger than the companies’ direct emissions. Therefore, a systematic literature review was carried out to understand how previous research in the field of supply chain management (SCM) approached energy efficiency and energy management in the upstream supply chain. The results indicate that only a few studies have addressed energy efficiency within SCM, and the majority assess energy solely as total energy use, without exploring opportunities to reduce final energy use among upstream suppliers. Moreover, results show that there is a lack of standards and methods to assess suppliers’ energy use, making it difficult for suppliers, particularly small and medium-sized enterprises, to overcome barriers to implementing energy management. In conclusion, even though energy efficiency is a cost-effective means to reduce CO2 emissions, only a limited number of studies in the field of SCM have integrated energy efficiency, revealing a vast knowledge gap in this field.

Renovation Analysis of a Socialist Modernism Office Building–Case Study

Socialist modernist architecture in the Balkan has left a valuable legacy of remarkable buildings from this century, which brought a mixture of new experiences and local traditions. Unfortunately, after the 1990s, many of these buildings have been neglected, improperly treated, or abandoned. This paper focuses on the iconic socialist modernist office building “Rilindja” in Prishtina, Kosovo, which was built in 1979. In 2010, the building envelope was changed in the name of revitalization, without considering the energy aspect. The study aims to present energy performance analysis through cost-optimal renovation measures of the building envelope while restoring the architectural values before the last renovation. Using Archicad and EcoDesigner STAR, a BIM model was created, and energy analysis was conducted. The findings indicate that eighteen proposed energy efficiency measures can achieve a reduction in energy consumption for heating and cooling of more than 80% over the life cycle period of 20 years. The comparison before and after renovation considering restoring and preserving the inherent values, while integrating energy efficiency measures was necessary, as there is a lack of such studies in the general literature. Finally, the potential of restoring the collective memory through cost-optimal analysis is emphasized as an approach for similar cases.

Advances in Research and Technology of Hydrothermal Carbonization: Achievements and Future Directions

Hydrothermal carbonization (HTC) has emerged as a pivotal technology in the battle against climate change and fosters circular economies. Operating within a unique reaction environment characterized by water as a solvent and moderate temperatures at self-generated pressures, HTC efficiently converts biomass residues into valuable bio-based products. Despite HTC’s potential—from the management of challenging biomass wastes to the synthesis of advanced carbons and the implementation of biorefineries—it encounters hurdles transitioning from academic exploration to industrial implementation. Gaps persist, from a general comprehension of reaction intricacies to the difficulty of large-scale integration with wastewater treatments, to the management of process water, to the absence of standardized assessment techniques for HTC products. Addressing these challenges demands collaboration to bridge the many scientific sectors touched by HTC. Thus, this article reviews the current state of some hot topics considered crucial for HTC development: It emphasizes the role of HTC as a cornerstone for waste management and biorefineries, highlighting potentialities and challenges for its development. In particular, it surveys fundamental research aspects, delving into reaction pathways, predictive models, analytical techniques, and HTC modifications while exploring HTC’s crucial technological applications and challenges, with a peculiar focus on combined HTC, wastewater integration, and plant energy efficiency.

Review of Hardware Platforms for Designing of Internet of Things (IoT) system

Microcontrollers are pivotal components in the realm of the Internet of Things (IoT), providing necessary computational power, memory, and connectivity for a wide range of applications. This review explores the key characteristics of microcontrollers that make suitable for IoT, considering their wide use and new emerging models. Study delves into the fundamental features required for IoT applications, such as low power consumption, compact form factors, diverse connectivity options, and flexible I/O (input-output) capabilities. The review covers a spectrum of microcontroller families from leading manufacturers, assessing their performance, energy efficiency, scalability, and ease of integration with various external devices for automation purpose.

Sensitivity and sustainability analysis of combined cycle plant with integrated LNG pressure energy recovery

Although combined cycle power plants (CCPPs) and liquefied natural gas (LNG) have arisen as solutions to the intermittent nature of renewable energy sources, they still need creative methods for performance enhancement and sustainability of the energy systems. Moreover, the energy efficiency of CCPPs is negatively impacted by environmental conditions, higher specific fuel consumption and a large quantity of waste heat released into the ambient. On the other hand, using seawater as a heating medium in LNG regasification circuits has a detrimental effect on the marine ecosystem. In this context, the LNG pressure energy recovery system through a direct expansion cycle (DEC) integrated with CCPP is proposed to improve sustainability, enhance exergetic performance and to reduce the heat recovery steam generator (HRSG) exhaust loss. To improve the cycle performance, low grade heat harnessed from CCPP is used as the heat source in the DEC rather than seawater. The system performance is analyzed by the energy and exergy method through a mathematical model. Moreover, the sensitivity analysis of the combined system is accomplished to examine the influence of LNG flow rate and ambient temperature on various performance indicators of the total system. It is observed that the integration of DEC boosts the HRSG performance by reducing the stack exergy loss and the efficiency is sensitive to LNG flow rate. The total integrated system energy efficiency ηtot, enhanced to 1.55% compared to the base case. Whereas, for ambient temperature in range of 15 – 45 °C, the system minimum and maximum exergetic efficiency ηex,tot is found to be 54.52 % and 55.94 %. Furthermore, the comparison of exergetic sustainability indicators is calculated and presented for three different simulation cases. The results demonstrate that increase in ηex,tot, affects the environment impact factor, ref and the sustainability index, θsi and improved 2.09 % compared to base case combined cycle.

Cost Benefits of Net Zero Energy Homes in Australia

This paper presents a systematic analysis of energy savings and cost benefits associated with several options for integrating energy efficiency and renewable energy technologies. The primary goal of this study is to assess the cost-effectiveness of achieving optimal net-zero energy (NZE) designs for residential buildings in Australia. Specifically, the analysis combines a series of sensitivity analyses and multi-objective optimizations to account for a wide range of design strategies for detached homes in four cities representing different Australian climates. The results indicate that not only are NZE designs technically feasible for all the considered Australian cities, but they are also highly cost-effective. This cost-effectiveness is attributed to the lower installation costs of rooftop PV systems as well as the beneficial interactive effects of proven energy efficiency strategies. Indeed, it is found that the deployment costs of rooftop PV systems can be recovered in less than 4 years. Moreover, the addition of thermal insulation in walls and ceilings can reduce both HVAC capacities and annual energy end-use by up to 59%. Based on an optimization-based design, NZE homes in Australia can have lower construction costs and, ultimately, lower life cycle costs than dwellings built to meet current energy efficiency standards based primarily on stringent building envelope thermal performance.

Vertically Stackable Ovonic Threshold Switch Oscillator Using Atomic Layer Deposited Ge0.6Se0.4 Film for High-Density Artificial Neural Networks.

Nanodevice oscillators (nano-oscillators) have received considerable attention to implement in neuromorphic computing as hardware because they can significantly improve the device integration density and energy efficiency compared to complementary metal oxide semiconductor circuit-based oscillators. This work demonstrates vertically stackable nano-oscillators using an ovonic threshold switch (OTS) for high-density neuromorphic hardware. A vertically stackable Ge0.6Se0.4 OTS-oscillator (VOTS-OSC) is fabricated with a vertical crossbar array structure by growing Ge0.6Se0.4 film conformally on a contact hole structure using atomic layer deposition. The VOTS-OSC can be vertically integrated onto peripheral circuits without causing thermal damage because the fabrication temperature is <400 °C. The fabricated device exhibits oscillation characteristics, which can serve as leaky integrate-and-fire neurons in spiking neural networks (SNNs) and coupled oscillators in oscillatory neural networks (ONNs). For practical applications, pattern recognition and vertex coloring are demonstrated with SNNs and ONNs, respectively, using semiempirical simulations. This structure increases the oscillator integration density significantly, enabling complex tasks with a large number of oscillators. Moreover, it can enhance the computational speed of neural networks due to its rapid switching speed.

Integrating energy efficiency and health safety in building design: A multi-objective optimization approach to minimize virus transmission risk

This study aims to develop an integrated design strategy for an office building in Tehran, optimizing energy efficiency and minimizing virus infection risk, addressing global challenges such as energy conservation and virus transmission. A multi-objective optimization model is developed to identify optimal design parameters for energy performance and infection risk reduction. Environmentally sustainable materials and energy-efficient equipment, such as solar panels, high-efficiency heat pump systems, and advanced air filtration techniques, are employed. The modified Wells-Riley Risk Model calculates infection risk, while energy efficiency is formulated based on the technologies used. Results showcase a significant 14% increase in energy efficiency and a 46% decrease in infection risk compared to traditional building designs, demonstrating the effectiveness of the proposed integrated approach. The optimal design parameters include ventilation rate, the number of solar panels, hot water pipe diameter, and heat pump condenser temperature. This study presents a practical, sustainable solution for designing office buildings that actively address pressing global concerns while fostering healthier, energy-efficient environments. The novelty of this research lies in the development of a multi-objective optimization model that synergizes infection control and energy efficiency, accounting for real equipment parameters and providing a new paradigm for HVAC design. This integrated strategy optimizes decision variables like ventilation rate, filtration level, solar panels, and heat pump operating conditions, offering a comprehensive approach to co-optimize infection control and energy performance.

ANN‐Enhanced Energy Reference Models for Industrial Buildings: Multinational Company Case Study

This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. It demonstrates that combining nonlinear regression with neural network modeling enhances predictive accuracy while maintaining the inherent simplicity of ANNs. Industrial sectors are increasingly adopting environmentally friendly practices, driven by the recognition that sustainable initiatives can lead to significant and lasting financial benefits rather than merely a sense of ecological duty. Integrating energy efficiency practices offers potential advantages in waste reduction and resource conservation, which can decrease operating expenses over time. This contributes significantly to pollution mitigation by reducing overall energy consumption cost‐effectively. Numerical simulations based on experimental results validate the proposed method, addressing the complexity and accuracy challenges in business models within the energy sector.

Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning

In the era of Industry 4.0, achieving both energy efficiency and robust security in Cyber-Physical Systems (CPSs) presents a significant challenge because of the resource requirements and complexity of these systems. This paper presents a novel method to integrate energy efficiency and robust security measures in CPSs. We propose the integration of anomaly detection techniques into the CPSs, to facilitate self-adaptation to changing conditions and threats, thereby enhancing system flexibility and reliability while also optimizing energy consumption. Our approach enhances the flexibility and reliability of CPSs by integrating Deep Reinforcement Learning (DRL) into the MAPE-K (Monitor-Analyze-Plan-Execute with Knowledge) control loop. This integration not only streamlines anomaly detection but also optimizes energy consumption, ensuring efficient and effective management of critical system functions. The outcome is a marked improvement in the adaptive decision-making capabilities of CPSs, leading to heightened security and better energy efficiency across various sectors and applications. This study significantly advances sustainable industrial practices within the Industry 4.0 paradigm, emphasizing the development of CPSs that excel in both energy efficiency and robust security.

(Invited) BEOL-Compatible Oxide Semiconductor Logic and Memory Devices

3D monolithic integration of logic and memory devices has emerged as one of the key enablers for next-generation electronic devices to address the ever-increasing demand for higher integration density, better performance and energy efficiency. BEOL-compatible oxide semiconductors show great potential to revolutionize the field thanks to their unique properties [1].We present our recent advancement related to BEOL-compatible oxide semiconductors for logic and memory applications. The digital-etch-enabled nanowire transistors and α-IGZO-based eDRAM will first be discussed, followed by the ferroelectric (FE) memories with high performance and novel structures utilizing α-IGZO or ALD-deposited ZnO as the channel.Ultra-scaled amorphous IGZO (α-IGZO) nanowire field-effect transistors (NW-FETs) hold great potential for applications demanding high performance and integration density. To realize the fabrication of high-quality and aggressively-scaled nanowire structures, a novel digital etching method for amorphous α-IGZO materials has been proposed and demonstrated [2]. Confirmed by the SEM images of an α-IGZO nanowire before and after digital etching, a notable nanowire width W NW reduction can be observed. We have the thinnest α-IGZO nanowire achieved by the digital etching with a W NW of approximately 20 nm. By further developing the transistor upon the nanowire, the α-IGZO NW-FET attains a good subthreshold swing (SS) of 80 mV/decade and a high peak extrinsic transconductance (G m, ext) of 612 μS/μm at V DS of 2 V (456 μS/μm at V DS = 1 V). Compared to the previous studies, our IGZO NW-FET achieves one of the highest peak G m values among all IGZO-based FETs.Besides the BEOL compatibility and high on-current, ultra-low subthreshold leakage makes α-IGZO FETs extremely promising for eDRAM. We further investigated the potential of α-IGZO eDRAM by developing the charge-domain compute-in-memory (CiM) [3]. Experiments have demonstrated small SS, large on-state current, and long-time charge retention with our dedicated 4T1C memory cell. With differential cell structure, an even higher tolerance for charge loss can be attained. With experiment-calibrated benchmarking in the VGG-8 network for CIFAR-10 image classification tasks, 2092 TOPS/W power efficiency for the CiM core can be expected, outperforming the prior TFT and CMOS-based CiM approaches, exhibiting significant advantages for ultra-low-power applications.The integration of doped-HfO2 FE material and oxide semiconductor, both BEOL-compatible with large-scale and cost-effective deposition, presents a promising avenue for advancing data storage in the future. We have developed the high-performance α-IGZO Fe-FET with a metal-ferroelectric-metal-oxide-semiconductor (MFMIS) structure as well as the Fe TCAM [4]. The α-IGZO Fe-FET achieves a large memory window of ~3 V with high reliability, while the Fe TCAM reduces the transistor number from 16 to 2 compared to the traditional SRAM-based one. Besides α-IGZO, we believe that ALD-based oxide semiconductors featuring high controllability of film thickness and conformal coverage of the 3D structures can create new opportunities for novel device structure and integration. The fin-gate ZnO Fe-FET stands for a great example while not only outstanding device performance but also suppressed device-to-device variation due to the unique structure have been demonstrated, holding tremendous promise for high-density 3D integration [5]. Acknowledgments: This work is supported by Singapore Ministry of Education (Tier 2: MOE2018-T2-2-154, Tier 1: R-263-000-D65-114).

Comparison of Battery Models Integrating Energy Efficiency and Aging for the Design of Microgrids

Comparison of Battery Models Integrating Energy Efficiency and Aging for the Design of Microgrids