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1. Self-sustained green neuromorphic interfaces

   https://doi.org/10.1038/s41467-021-23744-2  

   Fu, Tianda; Liu, Xiaomeng; Fu, Shuai; Woodard, Trevor; Gao, Hongyan; Lovley, Derek R.; Yao, Jun (December 2021, Nature Communications)

   null (Ed.)

   Abstract Incorporating neuromorphic electronics in bioelectronic interfaces can provide intelligent responsiveness to environments. However, the signal mismatch between the environmental stimuli and driving amplitude in neuromorphic devices has limited the functional versatility and energy sustainability. Here we demonstrate multifunctional, self-sustained neuromorphic interfaces by achieving signal matching at the biological level. The advances rely on the unique properties of microbially produced protein nanowires, which enable both bio-amplitude (e.g., <100 mV) signal processing and energy harvesting from ambient humidity. Integrating protein nanowire-based sensors, energy devices and memristors of bio-amplitude functions yields flexible, self-powered neuromorphic interfaces that can intelligently interpret biologically relevant stimuli for smart responses. These features, coupled with the fact that protein nanowires are a green biomaterial of potential diverse functionalities, take the interfaces a step closer to biological integration. 

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2. Bioinspired bio-voltage memristors

   https://doi.org/10.1038/s41467-020-15759-y  

   Fu, T; Liu, X; Gao, H; Ward, J. E; Liu, X; Yin, B; Wang, Z; Zhuo, Y; Walker, D; Yang, J. J; et al (April 2020, Nature communications)

   null (Ed.)

   Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated. 

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3. 256‐level honey memristor‐based in‐memory neuromorphic system

   https://doi.org/10.1049/ell2.70029  

   Uppaluru, Harshvardhan; Templin, Zoe; Khan, Mohammed_Rafeeq; Faruque, Md_Omar; Zhao, Feng; Wang, Jinhui (September 2024, Electronics Letters)

   Abstract Promising synaptic behaviour has been exhibited by memristors based on natural organic materials. Such memristor‐based neuromorphic systems offer notable benefits, including environmental sustainability, low production and disposal costs, non‐volatile storage capability, and bio/Complementary Metal‐Oxide‐Semiconductor (CMOS) compatibility. Here, a 256‐level honey memristor‐based neuromorphic system is experimentally evaluated for image recognition. In detail, first, 256‐level honey memristors are manufactured and tested based on in‐house technology; next, the non‐linear characteristics and inherent variation of honey memristor devices, which lead to imprecise weight updates and limit the inference accuracy, are investigated. Experimental results indicate that the inference accuracy of the 256‐level honey memristor‐based neuromorphic system is greater than 88% without cycle‐to‐cycle variations and 87% with cycle‐to‐cycle variations for different optimization algorithms. The overall performance of optimization algorithms with and without variation is compared in terms of energy and latency, where the momentum algorithm consistently outperforms the rest of the algorithms. This 256‐level honey memristor is a promising alternative enabling sustainable neuromorphic systems, encouraging further research into natural organic materials for neuromorphic computing. 

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4. Low-voltage short-channel MoS2 memtransistors with high gate-tunability

   https://doi.org/10.1557/s43578-024-01343-3  

   Liu, Stephanie E; Zeng, Thomas T; Wu, Ruiqin; Sangwan, Vinod K; Hersam, Mark C (May 2024, Journal of Materials Research)

   Neuromorphic hardware promises to revolutionize information technology with brain-inspired parallel processing, in-memory computing, and energy-efficient implementation of artificial intelligence and machine learning. In particular, two-dimensional (2D) memtransistors enable gate-tunable non-volatile memory, bio-realistic synaptic phenomena, and atomically thin scaling. However, previously reported 2D memtransistors have not achieved low operating voltages without compromising gate-tunability. Here, we overcome this limitation by demonstrating MoS2 memtransistors with short channel lengths < 400 nm, low operating voltages < 1 V, and high field-effect switching ratios > 10,000 while concurrently achieving strong memristive responses. This functionality is realized by fabricating back-gated memtransistors using highly polycrystalline monolayer MoS2 channels on high-κ Al2O3 dielectric layers. Finite-element simulations confirm enhanced electrostatic modulation near the channel contacts, which reduces operating voltages without compromising memristive or field-effect switching. Overall, this work demonstrates a pathway for reducing the size and power consumption of 2D memtransistors as is required for ultrahigh-density integration. 

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5. Memristive devices with short-term and long-term memory behaviors for processing temporal information

   https://doi.org/10.1063/5.0175200  

   Ki, Seung Jun; Kim, Jisoo; Chen, Mingze; Liang, Xiaogan (November 2023, Applied Physics Letters)

   Memristors based on 2D semiconductors such as MoS2 and its derivative materials exhibit analog switching behaviors capable of emulating some synaptic functions, including short-term plasticity, long-term potentiation, and spike-time-dependent-plasticity. Additional investigation is needed to realize reliable control of such synaptic behaviors for practical device implementation. To meet this scientific need, we fabricated MoS2-based memristors and studied their paired-pulse facilitation (PPF) and long-term memory characteristics under different pulse programming settings. This research has provided a guideline for identifying the programming settings for different neuromorphic processes. For example, a specific setting resulting in PPF > 30% and long-term conductance change < 20% has been identified to be suited for processing real-time temporal information. Furthermore, this research also indicates that the MoS2 memristor keeps having an almost constant relative change in conductance but greatly enhanced drive current level under laser illumination. This behavior can enable an easy integration of such memristive devices with state-of-the-art controller circuits for practice neuromorphic control applications. 

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