Neuromorphics

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Defect-Engineered Mn-Doped ZnO Memristors for Hardware-Aware Neuromorphic Image Classification

Defect-Engineered Mn-Doped ZnO Memristors for Hardware-Aware Neuromorphic Image Classification 150 150
Author(s): Richa Bharti, Lipika Sasmal, Shrestha Chaturvedi, Ajay D. Thakur

Abstract:

The practical deployment of neuromorphic hardware requires active materials with precisely controlled defect dynamics to govern charge transport and synaptic plasticity. While pristine ZnO offers a structurally favorable switching platform, the stochastic distribution of native defects often results in poor switching uniformity and compromised retention. In this study, we utilize …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 1 – 1
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3736551
Publisher: IEEE

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Raisu: A Radiation-Tolerant Neuromorphic Imager With In-Pixel Self-Healing and Unified Spiking Processing for Spaceborne Vision

Raisu: A Radiation-Tolerant Neuromorphic Imager With In-Pixel Self-Healing and Unified Spiking Processing for Spaceborne Vision 150 150
Author(s): Quan Cheng, Qiufeng Li, Zhengke Yang, Zhen Kong, Gaoqiang Niu, Yuan Liang, Jiamin Li, Jeong Hoan Park, Wang Liao, Hiromitsu Awano, Takashi Sato, Longyang Lin, Masanori Hashimoto

Abstract:

Spaceborne imaging systems operate under severe radiation and energy constraints, where single-event effects (SEEs), total ionizing dose (TID), and displacement damage dose (DDD) critically degrade reliability and performance. This work presents a radiation-tolerant neuromorphic imager prototype featuring a fully spike-based neuromorphic vision architecture with in-pixel self-healing capability and an integrated …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3715724
Publisher: IEEE

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Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning

Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning 150 150
Author(s): M. Drouhin, M. Grelier, M. Deroo, Y. Sassi, E. Monteblanco, N. Davila, G. Mihajlović, R. V. Chopdekar, T. S. Santos, J. A. Katine, D. Ravelosona

Abstract:

Artificial neural networks (ANNs) have enabled major advances in artificial intelligence, yet their growing computational and energy demands challenge conventional von Neumann architectures due to the costly separation of memory and processing. In-memory computing has emerged as a promising solution, particularly through memristive crossbar arrays capable of performing multiply-and-accumulate operations …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 129 – 134
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3702001
Publisher: IEEE

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Benchmarking of Emerging Material-Based TCAMs

Benchmarking of Emerging Material-Based TCAMs 150 150
Author(s): Ashwin Tunga, Junzhe Kang, Zijing Zhao, Ankit Shukla, Wenjuan Zhu, Shaloo Rakheja

Abstract:

This work presents a comprehensive benchmarking of ternary content-addressable memory (TCAM) implementations using timing-accurate SPICE simulations, systematically comparing conventional CMOS designs with emerging device technologies, including magnetic tunnel junctions (MTJs), ferroelectric tunnel junctions (FTJs), ferroelectric field-effect transistors (FeFETs), and 2-D reconfigurable field-effect transistors (2D RFETs). Key performance metrics, including search …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 104 – 112
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3687143
Publisher: IEEE

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OISMA: On-the-Fly In-Memory Stochastic Multiplication Architecture for Approximate Matrix Multiplication

OISMA: On-the-Fly In-Memory Stochastic Multiplication Architecture for Approximate Matrix Multiplication 150 150
Author(s): Shady Agwa, Yihan Pan, Georgios Papandroulidakis, Themis Prodromakis

Abstract:

Artificial intelligence (AI) models are currently driven by a significant upscaling of their complexity, with massive matrix-multiplication workloads representing the major computational bottleneck. In-memory computing (IMC) architectures are proposed to avoid the von Neumann bottleneck. However, both digital/binary-based and analog IMC architectures suffer from various limitations, which significantly degrade …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 56 – 64
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3680281
Publisher: IEEE

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