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1. Knowledge gaps for neuromorphic ionic computing

   https://doi.org/10.1126/science.aea2097  

   Aluru, Narayana R; Darling, Seth B; Elam, Jeffrey W; Gang, Oleg; Salleo, Alberto; Siwy, Zuzanna; Talin, A Alec; Noy, Aleksandr (May 2026, Science)

   Neuromorphic ionic computing is inspired by the brain’s use of ions for ultralow-energy computation—its massive parallelism, adaptability, and learning capabilities. This emerging paradigm can overcome limitations of conventional silicon-based computing by enabling colocated memory and processing, multicarrier information streams, and massive three-dimensional connectivity. However, substantial knowledge gaps remain in understanding and engineering ionic transport, energy dissipation, materials design, and scalable device architectures. This Review explores these critical challenges across seven key domains, highlighting the need for new theoretical approaches, materials, device concepts, and fabrication strategies. We argue that advancing ionic neuromorphic systems requires an interdisciplinary approach, integrating insights from biology and neuroscience, nanofluidics, materials science, and systems engineering to enable a new class of energy-efficient, robust, and reconfigurable computing technologies. 

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2. Emerging Memory Devices Beyond Conventional Data Storage: Paving the Path for Energy-Efficient Brain-Inspired Computing

   https://doi.org/10.1149/2.F10231IF  

   Jha, Rashmi (March 2023, The Electrochemical Society Interface)

   The current state of neuromorphic computing broadly encompasses domain-specific computing architectures designed to accelerate machine learning (ML) and artificial intelligence (AI) algorithms. As is well known, AI/ML algorithms are limited by memory bandwidth. Novel computing architectures are necessary to overcome this limitation. There are several options that are currently under investigation using both mature and emerging memory technologies. For example, mature memory technologies such as high-bandwidth memories (HBMs) are integrated with logic units on the same die to bring memory closer to the computing units. There are also research efforts where in-memory computing architectures have been implemented using DRAMs or flash memory technologies. However, DRAMs suffer from scaling limitations, while flash memory devices suffer from endurance issues. Additionally, in spite of this significant progress, the massive energy consumption needed in neuromorphic processors while meeting the required training and inferencing performance for AI/ML algorithms for future applications needs to be addressed. On the AI/ML algorithm side, there are several pending issues such as life-long learning, explainability, context-based decision making, multimodal association of data, adaptation to address personalized responses, and resiliency. These unresolved challenges in AI/ML have led researchers to explore brain-inspired computing architectures and paradigms. 

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3. Two-Dimensional Materials for Brain-Inspired Computing Hardware

   https://doi.org/10.1021/acs.chemrev.4c00631  

   Hadke, Shreyash; Kang, Min-A; Sangwan, Vinod K; Hersam, Mark C (January 2025, Chemical Reviews)

   Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions. 

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4. Building Scalable Silicon Microring Resonator‐Based Neuromorphic Photonic Circuits Using Post‐Fabrication Processing with Photochromic Material

   https://doi.org/10.1002/adom.202402706  

   Xu, Lei; Zhang, Jiawei; Doris, Eli A; Bilodeau, Simon; Wisch, Jesse A; Gui, Manting; Jimoh, Yusuf O; Shastri, Bhavin; Rand, Barry P; Prucnal, Paul R (April 2025, Advanced Optical Materials)

   Abstract Neuromorphic photonics has become one of the research forefronts in photonics, with its benefits in low‐latency signal processing and potential in significant energy consumption reduction when compared with digital electronics. With artificial intelligence (AI) computing accelerators in high demand, one of the high‐impact research goals is to build scalable neuromorphic photonic integrated circuits which can accelerate the computing of AI models at high energy efficiency. A complete neuromorphic photonic computing system comprises seven stacks: materials, devices, circuits, microarchitecture, system architecture, algorithms, and applications. Here, we consider microring resonator (MRR)‐based network designs toward building scalable silicon integrated photonic neural networks (PNN), and variations of MRR resonance wavelength from the fabrication process and their impact on PNN scalability. Further, post‐fabrication processing using organic photochromic layers over the silicon platform is shown to be effective for trimming MRR resonance wavelength variation, which can significantly reduce energy consumption from the MRR‐based PNN configuration. Post‐fabrication processing with photochromic materials to compensate for the variation in MRR fabrication will allow a scalable silicon system on a chip without sacrificing today's performance metrics, which will be critical for the commercial viability and volume production of large‐scale silicon photonic circuits. 

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5. Low-Dimensional Nanoelectronic Materials for Energy-Efficient Edge Computing

   https://doi.org/10.1149/2.F13254IF  

   Hadke, Shreyash S; Sangwan, Vinod K; Hersam, Mark C (December 2025, The Electrochemical Society Interface)

   The rapid deployment of AI agents in IoT, healthcare, robotics, and consumer electronics, combined with the stalled scaling and limited energy efficiency of silicon CMOS computing architectures, have placed unsustainable demands on electricity and water use in data centers. These problems are exacerbated for edge computing applications that require on-chip learning in evolving environments. Recent research in 1D and 2D nanomaterials exemplifies co-design approaches to discover and optimize new materials, devices, circuit architectures, and algorithms. These low-dimensional nanomaterials have shown promise for the realization of high-performance digital electronics and bioinspired approaches for energy-efficient edge AI. This work is fueled by a growing library of low-dimensional nanoelectronic materials, 3D integration, and vdW heterojunctions, resulting in simplified and reconfigurable neuromorphic circuits. 

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