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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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Operational Coordination Optimization of Electricity and Natural Gas Networks Based on Sequential Symmetrical Second-Order Cone programming

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2025年第2期13卷 488-499页

作者： Min, Liang Lou, Chengwei Yang, Jin Yu, James Yu, Zhibin Power Grid Dispatching & Control Branch Co NARI Te Nanjing Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China Univ Glasgow Sch Engn Glasgow G12 8QQ Scotland Scottish Power Energy Network Glasgow G2 5AD Scotland Univ Liverpool Dept Mech & Aerosp Engn Liverpool England

The variable and unpredictable nature of renewable energy generation (REG) presents challenges to its large-scale integration and the efficient and economic operation of the electricity network, particularly at the distribution level. In this paper, an operational coordination optimization method is proposed for the electricity and natural gas networks, aiming to overcome the identified negative impacts. The method involves the implementation of bi-directional energy flows through power-to-gas units and gas-fired power plants. A detailed model of the three-phase power distribution system up to each phase is employed to improve the representation of multi-energy systems to consider real-world end-user consumption. This method allows for the full consideration of unbalanced operational scenarios. Meanwhile, the natural gas network is modelled and analyzed with steady-state gas flows and the dynamics of the line pack in pipelines. The sequential symmetrical second-order cone programming (SS-SOCP) method is employed to facilitate the simultaneous analysis of three-phase imbalance and line pack while accelerating the solution process. The efficacy of the operational coordination optimization method is demonstrated in case studies comprising a modified IEEE 123-node power distribution system with a 20-node natural gas network. The studies show that the operational coordination optimization method can simultaneously minimize the total operational cost, the curtailment of installed REG, the voltage imbalance of three-phase power system, and the overall carbon emissions.

关键词： Natural gas Power systems Pipelines Electricity Optimization Mathematical models Load flow programming Numerical models Power system dynamics Operational coordination multi-energy system power-to-gas electricity network natural gas network second-order cone programming

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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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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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LiP-LLM: Integrating Linear programming and Dependency Graph With Large Language Models for Multi-Robot Task Planning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1122-1129页

作者： Obata, Kazuma Aoki, Tatsuya Horii, Takato Taniguchi, Tadahiro Nagai, Takayuki Osaka Univ Grad Sch Engn Sci Dept Syst Innovat Osaka 5650871 Japan Kyoto Univ Dept Informat Kyoto 6068501 Japan

This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. For multi-robots to efficiently perform tasks, it is necessary to manage the precedence dependencies between tasks. Although multi-robot decentralized and centralized task planners using LLMs have been proposed, none of these studies focus on precedence dependencies from the perspective of task efficiency or leverage traditional optimization methods. It addresses key challenges in managing dependencies between skills and optimizing task allocation. LiP-LLM consists of three steps: skill list generation and dependency graph generation by LLMs, as well as task allocation using linear programming. The LLMs are utilized to generate a comprehensive list of skills and to construct a dependency graph that maps the relationships and sequential constraints among these skills. To ensure the feasibility and efficiency of skill execution, the skill list is generated by calculated likelihood, and linear programming is used to optimally allocate tasks to each robot. Experimental evaluations in simulated environments demonstrate that this method outperforms existing task planners, achieving higher success rates and efficiency in executing complex, multi-robot tasks. The results indicate the potential of combining LLMs with optimization techniques to enhance the capabilities of multi-robot systems in executing coordinated tasks accurately and efficiently. In an environment with two robots, a maximum success rate difference of 0.82 is observed in the language instruction group with a change in the object name.

关键词： Robots Planning Resource management Linear programming Robot kinematics Natural languages Multi-robot systems Optimization programming Navigation task planning

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

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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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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 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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Fast Probabilistic Energy Flow Calculation for Natural Gas Systems: A Convex Multiparametric programming Approach

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 6786-6796页

作者： Jia, Wenhao Ding, Tao Yuan, Yi Zhang, Hongji Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Shaanxi Peoples R China

Probabilistic energy flow (PEF) calculation is a fundamental task for the operation and planning of both natural gas systems (NGSs) and integrated energy systems considering uncertainties. Traditional Monte Carlo simulation (MCS) based PEF approach requires repeated energy flow calculations based on a large number of random samples, leading to a huge computation burden. Hence, this paper proposes a convex multiparametric programming (MPP) based fast PEF calculation method for NGSs. First, we develop an energy function based convex optimization model whose optimal solution is equivalent to the solution of deterministic energy flow equations. Then, we propose a convex MPP model with uncertain boundary conditions (such as the gas injections/loads) as the parameters. A multiparametric quadratic approximation algorithm is further introduced to solve the proposed MPP and obtain the analytical energy flow expression. This analytical expression characterizes the mapping relationship between the energy flow solution and uncertain parameters. Finally, we establish an online PEF calculation framework, in which the repeat energy flow calculations can be efficiently performed by merely substituting the uncertain parameters into the analytical energy flow expression obtained offline. Case studies on multiple NGSs verify the effectiveness of the proposed method. Note to Practitioners-This paper introduces a novel approach for efficient PEF calculation in NGSs that addresses the computational challenges posed by traditional methods. Conventional MCS-based PEF approaches involve numerous energy flow calculations with massive stochastic scenarios, resulting in significant computational burdens. The key contribution of our work is to equivalently reformulate the original PEF problem as an energy function based convex MPP model, which incorporates uncertain boundary conditions (e.g., gas injections/loads) as its parameters. Then, a multiparametric quadratic approximation algorithm is s

关键词： Mathematical models Boundary conditions Natural gas programming Probabilistic logic Iterative methods Computational efficiency Integrated energy systems natural gas systems probabilistic energy flow multiparametric programming

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A Compelling Image: The Tower of Babel and the Proliferation of programming Languages During the 1960s

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IEEE ANNALS OF THE HISTORY OF COMPUTING 2025年第1期47卷 22-35页

作者： Nofre, David Independent Scholar Oegstgeest Leiden The Netherlands

In 1961 and in 1969, two towers of Babel of programming languages were published. The first one appeared on the cover of Communications of the ACM. The second one appeared on Jean E. Sammet's programming Languages: History and Fundamentals. These two towers have come to symbolize the fragmentation that plagued the development of programming as a result of the multiplicity of notations. I will argue that contrary to this common view, the tower on the cover of Communications should be understood as a proud display of the research that profoundly transformed computer programming during the late 1950s. The tower of Sammet's book reflected instead the book's goal of bringing order into the field of programming languages. Symbolically, Sammet's book marked the end of a decade that witnessed the emergence of a logico-linguistic approach to the study of programming notations, a decade that also spurred Sammet's fascination with Babel.

关键词： Poles and towers Computer languages programming Painting Visualization History Symbols

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