Circuits

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Integrating Atomistic Insights With Circuit Simulations via Transformer-Driven Symbolic Regression

Integrating Atomistic Insights With Circuit Simulations via Transformer-Driven Symbolic Regression 150 150
Author(s): Md Rahatul Islam Udoy, Jack Hutchins, Shamiul Alam, Catherine Schuman, Ahmedullah Aziz

Abstract:

This article introduces a framework that establishes a cohesive link between the first principles-based simulations and circuit-level analyses using a machine learning-based compact modeling platform. Starting with atomistic simulations, the framework examines the microscopic details of material behavior, forming the foundation for later stages. The generated datasets, with molecular insights, …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 179 – 187
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3624662
Publisher: IEEE

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Quantum Field Theory Model for Spin-Based Devices Using 2-D van der Waals Materials

Quantum Field Theory Model for Spin-Based Devices Using 2-D van der Waals Materials 150 150
Author(s): Axel I. Saenz Rodriguez, Oksana Ostroverkhova, Pallavi Dhagat

Abstract:

We explore the effects of layered geometries of 2-D quantum spin systems as a method to tune and control material properties for spintronic devices. We analyze the dispersion relation of a 2-D quantum spin system with a shifted bilayer square lattice through the linear spin wave (LSW) approximation of quantum …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 171 – 178
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3624217
Publisher: IEEE

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1.58-b FeFET-Based Ternary Neural Networks: Achieving Robust Compute-In-Memory With Weight-Input Transformations

1.58-b FeFET-Based Ternary Neural Networks: Achieving Robust Compute-In-Memory With Weight-Input Transformations 150 150
Author(s): Imtiaz Ahmed, Akul Malhotra, Revanth Koduru, Sumeet Kumar Gupta

Abstract:

Ternary weight neural networks (TWNs), with weights quantized to three states (−1, 0, and 1), have emerged as promising solutions for resource-constrained edge artificial intelligence (AI) platforms due to their high energy efficiency with acceptable inference accuracy. Further energy savings can be achieved with TWN accelerators utilizing techniques such as compute-in-memory (CiM) and …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 157 – 165
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3621160
Publisher: IEEE

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Non-Volatile ReRAM-Based Compact Event-Triggered Counters

Non-Volatile ReRAM-Based Compact Event-Triggered Counters 150 150
Author(s): Moin Diwan, Shengchao Zhang, Zidu Li, Alex James, Bhaskar Choubey

Abstract:

With an increasing number of transistors per circuit, the fabrication cost and the energy consumption of each integrated circuits increase exponentially, which drives the need to reduce the number of transistors. In this study, we explore a novel design for a 16-bit digital counter that utilizes a combination of complementary …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 131 – 138
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3619415
Publisher: IEEE

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Benchmarking of FERAM-Based Memory System by Optimizing Ferroelectric Device Model

Benchmarking of FERAM-Based Memory System by Optimizing Ferroelectric Device Model 150 150
Author(s): Mohammad Adnaan, Saeideh Alinezhad Chamazcoti, Emil Karimov, Marie Garcia Bardon, Francky Catthoor, Jan van Houdt, Azad Naeemi

Abstract:

We present a framework for design technology co-optimization (DTCO) of the main memory system with one transistor-one capacitor (1T1C) ferroelectric random access memory (FERAM) as an alternative to dynamic random access memory (DRAM). We start with the ferroelectric capacitor device model and perform array-level memory circuit simulation. Then, we …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 99 – 106
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3618883
Publisher: IEEE

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A Bit-Cell Failure Analysis Framework for Ferroelectric Field-Effect Transistor-Based Memories

A Bit-Cell Failure Analysis Framework for Ferroelectric Field-Effect Transistor-Based Memories 150 150
Author(s): Jianze Wang, Wei Zhang, Xuanyao Fong

Abstract:

The ferroelectric field-effect transistor (FeFET) is a promising memory device technology due to desirable attributes, such as fast access times, high memory cell density, good endurance, compatibility with CMOS process, and impressive scalability. While previous research has explored the impact of process variations at the device level, their effects on …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 123 – 130
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3616007
Publisher: IEEE

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3-D Stacked HBM and Compute Accelerators for LLM: Optimizing Thermal Management and Power Delivery Efficiency

3-D Stacked HBM and Compute Accelerators for LLM: Optimizing Thermal Management and Power Delivery Efficiency 150 150
Author(s): Janak Sharda, Madison Manley, Jungyoun Kwak, Chinsung Park, Muhannad Bakir, Shimeng Yu

Abstract:

Advanced packaging is becoming essential for designing hardware accelerators for large language models (LLMs). Different architectures, such as 2.5-D integration of memory with logic, have been proposed; however, the bandwidth limits the throughput of the complete system. Recent works have proposed memory on logic systems, where high bandwidth memory (HBM) …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 116 – 122
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3617298
Publisher: IEEE

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A 28-nm Computing-in-Memory Processor With Zig-Zag Backbone-Systolic CIM and Block-/Self-Gating CAM for NN/Recommendation Applications

A 28-nm Computing-in-Memory Processor With Zig-Zag Backbone-Systolic CIM and Block-/Self-Gating CAM for NN/Recommendation Applications 150 150
Author(s): Shengzhe Yan, Zhuoyu Dai, Zhaori Cong, Zeyu Guo, Yifan He, Wenyu Sun, Chunmeng Dou, Feng Zhang, Jinshan Yue, Yongpan Liu, Ming Liu

Abstract:

Computing-in-memory (CIM) chips have demonstrated promising energy efficiency for artificial intelligence (AI) applications such as neural networks (NNs), Transformer, and recommendation system (RecSys). However, several challenges still exist. First, a large gap between the macro and system-level CIM energy efficiency is observed. Second, several memory-dominate operations, such as embedding in …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3610539
Publisher: IEEE

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A Fully-Dynamic Capacitive Touch Sensor With Tri-level Energy Recycling and Compressive Sensing Technique

A Fully-Dynamic Capacitive Touch Sensor With Tri-level Energy Recycling and Compressive Sensing Technique 150 150
Author(s): Xiangdong Feng, Zhiyu Wang, Haoyang Li, Jiaqing Li, Wei-Chin Lin, Xin Hu, Zhong Tang, Yuyan Liu, Qinwen Fan, Yuxuan Luo, Bo Zhao

Abstract:

Capacitive touch screens have become the dominant user interface over the past decade. Achieving high framerates with low power consumption remains a critical design goal for touch systems. The conventional charge-recycling technique reduces driving power by 64%, but it relies on off-chip capacitors. To address this issue, we propose a tri-level …

Published in: IEEE Solid-State Circuits Letters
Page(s): 1 – 1
Year of Publication: 2025
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3612093
Publisher: IEEE

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