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Effect of programming Schemes on Short-Term Instability in 1T1R Configuration

IEEE ACCESS 2025年 13卷 44555-44564页

作者： Liu, Xiaohua Chen, Yang Kopperberg, Nils Solfronk, Oliver Hoffmann-Eifert, Susanne Menzel, Stephan Waser, Rainer Wiefels, Stefan Forschungszentrum Julich Peter Grunberg Inst 7 D-52425 Julich Germany Rhein Westfal TH Aachen Inst Werkstoffe Elektrotech 2 D-52074 Aachen Germany Rhein Westfal TH Aachen JARA FIT D-52074 Aachen Germany Forschungszentrum Julich Peter Grunberg Inst 10 D-52425 Julich Germany

Short-term instability is one of the key challenges for redox-based resistive random-access memory (ReRAM) reaching practical applications in the field of neuromorphic computing. Developing programming schemes that balance resistance fine-tuning with short-term stability is crucial for improving the reliability of ReRAM devices. In this work, we explore the impact of programming schemes on the short-term variability of valence change mechanism (VCM)-based filamentary ReRAM devices. We design three programming schemes: a baseline multi-pulse scheme, a single short-pulse scheme, and a single long-pulse scheme. By comparing the programming statistics of the one-transistor-one-resistor (1T1R) structures under different schemes and analyzing their evolution over time, we propose the hypothesis that the number and width of pulses influence the filament size and internal distribution of oxygen vacancies, and thus the short-term stability of ReRAM devices. We employ three-dimensional Kinetic Monte Carlo simulation, constructing filaments of varying sizes and adjusting the oxygen vacancy distribution to simulate the outcomes of different programming schemes. The simulation results align with experimental findings. Based on existing experimental data and modeling studies, we propose programming strategies that balance programmability and reliability for VCM-based filamentary ReRAM devices.

关键词： programming Transistors Voltage Electrodes Solid modeling Three-dimensional displays Kinetic theory Stability criteria Resistance Monte Carlo methods Redox-based resistive random-access memory (ReRAM) programming schemes short-term stability simulation kinetic Monte Carlo (KMC)

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3-D Vertical Resistive Switching Random Access Memory (3D-VRRAM) With Multilevel programming for High-Density, Energy-Efficient In-Memory Computing

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IEEE TRANSACTIONS ON ELECTRON DEVICES 2025年第5期72卷 2677-2684页

作者： Bridarolli, D. Zucchelli, C. Mannocci, P. Ricci, S. Farronato, M. Pedretti, G. Sun, Z. Ielmini, D. Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB I-20133 Milan Italy IU NET I-20133 Milan Italy Hewlett Packard Labs Artificial Intelligence Res Lab AIRL Milpitas CA 95035 USA Peking Univ Inst Artificial Intelligence Sch Integrated Circuits Beijing 100871 Peoples R China

Resistive random access memory (RRAM) devices offer a broad range of attractive properties for in-memory computing (IMC) applications, such as nonvolatile storage, low read current, and high scalability. IMC allows to overcome the memory bottleneck of data-intensive workloads, such as deep learning on the edge. In this context, 3-D vertical RRAM (3D-VRRAM) is a promising option to achieve high memory cell capacity with low fabrication cost. In this work, we present an HfOx-based 3D-VRRAM crossbar array (CBA) capable of IMC with precise multilevel programming. We show an extensive experimental demonstration of both matrix-vector multiplication (MVM) and inverse/pseudoinverse matrix calculation via IMC on 3D-VRRAM. To further support the parallel IMC application in real-life scenarios, the work also reports a demonstration of relatively large-size problems adopting 2D-RRAM and SRAM-based memory arrays. These results support 3D-VRRAM for high-density, energy-efficient IMC for edge computing applications.

关键词： Semiconductor device measurement Three-dimensional displays Energy efficiency Electrodes Silicon compounds programming In-memory computing Hafnium compounds Current measurement Scanning electron microscopy Edge computing embedded memory in-memory computing (IMC) monolithic 3-D integration resistive random access memory (RRAM) scaling

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A Mix-Integer programming Based Deep Reinforcement Learning Framework for Optimal Dispatch of Energy Storage System in Distribution Networks

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2025年第2期13卷 597-608页

作者： Hou, Shengren Salazar, Edgar Mauricio Palensky, Peter Chen, Qixin Vergara, Pedro P. Delft Univ Technol Intelligent Elect Power Grids IEPG Grp NL-2628 CD Delft Netherlands Eindhoven Univ Technol Elect Energy Syst EES Grp Eindhoven Netherlands Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Operat & Control Beijing 100084 Peoples R China

The optimal dispatch of energy storage systems (ESSs) in distribution networks poses significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand, and the variability inherent in renewable energy sources. By exploiting the generalization capabilities of deep neural networks (DNNs), the deep reinforcement learning (DRL) algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks. Nevertheless, the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time, which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations. This paper introduces an innovative framework, named mixed-integer programming based deep reinforcement learning (MIP-DRL), to overcome these limitations. The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution. This framework involves training a Q-function with DNNs, which is subsequently represented in a mixed-integer programming (MIP) formulation. This unique combination allows for the seamless integration of operational constraints into the decision-making process. The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations, demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.

关键词： Distribution networks Training Real-time systems programming Microgrids Voltage control Artificial neural networks Voltage regulation optimal dispatch distribution network mixed-integer programming deep reinforcement learning (DRL) energy management

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A Second-Order Cone programming (SOCP) Based Co-Optimization Approach for Integrated Transmission-Distribution Optimal Power Flow

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 3563-3572页

作者： Chowdhury, Md Mahmud-Ul-Tarik Hasan, Md Shamim Kamalasadan, Sukumar Univ North Carolina Charlotte Energy Prod Infrastructure Ctr EP Dept Elect Engn Charlotte NC 28223 USA

The advancement of grid technology with high integration of distributed energy resources (DERs) has led to dynamic changes in the distribution network with a substantial impact on the transmission network. With this impact of DER integration, separate optimal power flow (OPF) analysis in transmission and distribution (TD) networks is not always adequate for the optimal operation of the power system. Hence, this paper proposes a coordinated TD-OPF co-optimization method with second-order cone programming (SOCP) to address this challenge. This co-optimization method provides a feasible solution for an integrated TD system, bridging the gap between transmission and distribution network challenges with renewable-based DERs through data exchange between the transmission and distribution networks. Moreover, each distribution network employs a distributed OPF (D-OPF) algorithm, which supports decentralized operation, thereby enhancing the reliability and scalability of the proposed OPF model for large TD networks. The goal is to reduce the overall system planning expenses, optimizing the microgrids in distribution networks and the major generation units within the transmission network. The efficacy of the proposed TD-OPF co-optimization approach has been verified through numerical analysis.

关键词： Distribution networks Load flow Computational modeling Reactive power Power systems Analytical models Mathematical models Distributed power generation Renewable energy sources programming Transmission-distribution Co-OPF distributed OPF angle & conic relaxation SOCP-OPF

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Students' Test Anxiety and Performance in Introductory programming: Do Exam and Assessment Modalities Play a Role?

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2025年第3期33卷

作者： Tomic, Bojan Stojanovic, Tatjana Antovic, Ilija Milic, Milos Univ Belgrade Fac Org Sci Belgrade Serbia

Many students encounter programming for the first time during undergraduate studies. programming, being different from conventional elementary and middle school subjects, maybe a source of anxiety and frustration for these students. It is already known that heightened anxiety can negatively impact test performance, that females are more test anxious than males, and that many introductory computer science courses suffer from high dropout rates. This is the second part of our two-fold study, which aims to answer whether exam and assessment modalities influence students' test anxiety and the (possible) correlation between test anxiety and performance in an introductory programming course. The research questions and most important findings are as follows: (i) female versus male students' test anxiety regarding a computer-based automatically assessed exam (female students exhibited higher levels of test anxiety than male students), (ii) test anxiety regarding a paper-based manually assessed exam versus test anxiety regarding a computer-based automatically assessed exam (test anxiety during a paper-based manually assessed programming exam was lower than the latter), (iii) general test anxiety versus test anxiety regarding a computer-based automatically assessed exam (only female students were more test anxious on a computer-based automatically assessed exam than in general), (iv) test anxiety regarding a paper-based manually assessed exam and exam score (no correlation found), and (v) test anxiety regarding a computer-based automatically assessed exam and exam score (statistically significant, moderately negative correlation found only for female students). Our findings suggest that efforts should be made toward addressing female students' test anxiety in introductory programming as their well-being and exam performance are affected the most.

关键词： automated assessment evaluation and assessment higher education programming test anxiety

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An interval two-stage robust stochastic programming under a bi-level multi-objective framework toward river basin water resources allocation

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COMPUTERS & OPERATIONS RESEARCH 2025年 180卷

作者： Tu, Yan Lu, Yongzheng Lev, Benjamin Wuhan Univ Technol Sch Safety Sci & Emergency Management Wuhan 430070 Peoples R China Drexel Univ LeBow Coll Business Philadelphia PA 19104 USA

The uncertainty stemming from hydrological variables and socio-economic parameters poses new challenges to river basin water resources allocation (RBWRA). Given the pressing need for efficient, environmentally friendly, and equitable solutions in RBWRA, a bi-level multi-objective interval two-stage robust stochastic programming (BLMOITRSP) model is proposed. This model aims to achieve the optimal balance among efficiency, eco-friendliness, and equity, collectively called the "3E". A novel hierarchical mixed water allocation mechanism is introduced within this model. The basin authority pursues the "3E" objectives at the macro- control level through administrative water allocation. Conversely, sub-areas as followers prioritize economic interests, striving for economic benefit maximization through water market allocation. Furthermore, uncertain parameters (e.g., water demand) are treated as interval parameters, employing interval two-stage robust stochastic programming (ITRSP) to address uncertainty issues and control systemic risks in the model. To solve the BLMOITRSP model, we present a bi-level interactive global equilibrium optimization algorithm, fusing with the modified particle swarm optimization (PSO) algorithm. The bi-level algorithm provides solutions tailored to the preferences of different decision-makers. The proposed model and method are also applied to the Hanjiang River Basin in China to demonstrate its feasibility and effectiveness. The results indicate that the proposed model effectively ensures the "3E" balance. The introduction of the hierarchical mixed water allocation mechanism proves conducive to promoting water distribution and enhancing economic benefits. ITRSP effectively controls the systemic risks of the model's impact on the basin's total economic benefit. The economic performance of each sub-area varies in response to different decision preferences under RBWRA schemes. Finally, the conclusions and future research directions are provided.

关键词： Interval two-stage robust stochastic programming Bi-level multi-objective optimization Decision preference River basin water resources allocation Hierarchical mixed water allocation mechanism

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A Linear programming Bound for Sum-Rank Metric Codes

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第1期71卷 317-329页

作者： Abiad, Aida Gavrilyuk, Alexander L. Khramova, Antonina P. Ponomarenko, Ilia Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands Vrije Univ Brussel Dept Math & Data Sci B-1050 Ixelles Belgium Shimane Univ Interdisciplinary Fac Sci & Engn Matsue 6908504 Japan Univ Memphis Dept Math Sci Memphis TN 38152 USA Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands Hainan Univ Sch Math & Stat Haikou 570228 Peoples R China

We derive a linear programming bound on the maximum cardinality of error-correcting codes in the sum-rank metric. Based on computational experiments on relatively small instances, we observe that the obtained bounds o... 详细信息

关键词： Measurement Codes Linear programming Extraterrestrial measurements Eigenvalues and eigenfunctions Vectors Upper bound Symmetric matrices programming Polynomials Sum-rank metric code linear programming bound sum-rank metric graph association scheme eigenvalues

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Analyzing the Benefits of Optical Topology programming for Mitigating Link-Flood DDoS Attacks

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2025年第1期22卷 146-163页

作者： Nance-Hall, Matthew Liu, Zaoxing Sekar, Vyas Durairajan, Ramakrishnan Univ Oregon Eugene OR 97403 USA Univ Maryland Comp Sci College Pk MD 20742 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

Link-flood attacks (LFAs) overwhelm bandwidth on links in a network using traffic from many sources, which is indistinguishable from benign traffic. Unfortunately, traditional DDoS defenses are incapable of stopping such attacks and recently proposed software-defined solutions are ineffective. In this work, we observe a new opportunity for mitigating LFAs using optical networking advances. In essence, we envision new capabilities for topology programming, to scale capacity on-demand to avoid congestion and add new links to the network to create new paths for traffic during LFA incidents. Realizing these benefits of optical topology programming raises unique challenges;the search space for candidate topology configurations is very large and joint optimization of topology and routing is NP-hard. We present ONSET-a framework that tackles these challenges to lay a practical foundation for topology programming-based defenses against LFAs. We show that ONSET complements existing programmable network defenses and amplifies their benefits. We perform a what-if style analysis of ONSET by simulating a wide-ranging set of attacks, including terabit-scale attacks against every single link, on five networks with two different routing capabilities and observe that ONSET provides the means to mitigate congestion loss in more than 90% of the hundreds of diverse attack scenarios considered.

关键词： Network topology Topology programming Optical fiber communication Routing Optical fiber networks Computer crime Computer simulation experiments DDoS defense optical networks wide area networks

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Supply chain viability by integrating R-imperatives, product development, and design decisions: A stochastic programming framework

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 136卷

作者： Darmian, Sobhan Mostafayi Sgarbossa, Fabio Fattahi, Mohammad Morande, Juan Pablo NTNU Dept Mech & Ind Engn Trondheim Norway Northumbria Univ Newcastle Business Sch Newcastle Upon Tyne England Auburn Univ Dept Ind & Syst Engn Auburn AL USA

This study focuses on operationalizing R-imperatives by integrating product development and design decisions in supply chains (SC) facing disruptive events. This approach leads to viable SCs that are sustainable, resilient to disruptions, adaptable to ongoing changes, and enhance circularity. We use a stochastic programming approach to propose this new planning framework. To measure SC resilience, we consider the operational cost increase during the recovery period following a potential disruption. Additionally, flexibility in product configuration after disruptions is introduced as a new contingency strategy in this study. Motivated by two European companies involved in a circular manufacturing initiative in Norway, we define disruption scenarios and assess the effectiveness of our framework. Our results show that SCs can improve both economically and in terms of circularity when R-imperatives, product development, and design decisions are integrated. Furthermore, under disruption events, SC resilience improves on average by 4.28 % and 5.07 % in these two case studies. We also highlight that budget savings from this approach can range between 2 % to 10 % when a SC adapts to increased demand.

关键词： Viability Product development Supply chain design Circular economy Two-stage stochastic programming

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A Column-Parallel On-programming Pixel-Current Readout Embedded in an OLED Display Driver IC

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2025年第4期60卷 1174-1189页

作者： Lim, Gyu-Wan Kim, Dong-Kyu Kang, Gyeong-Gu Shin, Seunghwa Kim, Kihyun Park, Yousung Kim, Won Kim, Young-Bok Jeon, Hyun-Kyu Kim, Hyun-Sik Korea Adv Inst Sci & Technol KAIST Sch Elect Engn Daejeon 34141 South Korea LX Semicon Co Ltd Seoul 06267 South Korea

This article presents an organic light-emitting diode (OLED) driver IC that embeds a column-parallel real-time pixel-current readout circuit for sensing current during actual display operation. It features a folding integrator (FI) for high sensitivity and wide dynamic range (DR) in current sensing, along with a source-driven pixel and an AFE-combined voltage driver (ACD) to facilitate current sensing during display data programming. A channel-shared common-mode noise rejection filter (CMRF) is also presented to effectively suppress channel-common panel noise. The 16-channel prototype chip was fabricated using a 180-nm CMOS process. Measurement results show that the FI achieves 80-mV/nA sensitivity and a 16-fold DR extension, while the CMRF offers up to 63 dB of noise rejection. The 11-bit current readout exhibits a maximum differential nonlinearity (DNL) of 0.33 LSB, an integral nonlinearity (INL) of 1.7 LSB, an rms noise of 1.98 digital number (DN), and a signal-to-noise ratio (SNR) of 60.3 dB. Real demonstrations with LED also validate the pixel-current sensing functionality and performance of the proposed chip.

关键词： Organic light emitting diodes Thin film transistors Voltage Sensors Sensitivity Noise Real-time systems programming Manufacturing Information and communication technology Common-mode noise rejection current sensing display driver IC folding integrator (FI) full-scale range non-uniformity compensation organic light-emitting diode (OLED) readout real time source-driven pixel thin-film transistor (TFT)

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