Solid-state neuromorphic systems based on transistors or memristors have yet to achieve the interconnectivity, performance, and energy efficiency of the brain due to excessive noise, undesirable material properties, and nonbiological switching mechanisms. Here we demonstrate that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing. Unlike state-of-the-art devices, our two-terminal, biomolecular memristor features similar structure (biomembrane), switching mechanism (ion channels), and ionic transport modality as biological synapses while operating at considerably lower power. The reversible and volatile voltage-driven insertion of alamethicin peptides into an insulating lipid bilayer creates conductive pathways that exhibit pinched current-voltage hysteresis at potentials above their insertion threshold. Moreover, the synapse-like dynamic properties of the biomolecular memristor allow for simplified learning circuit implementations. Low-power memristive devices based on stimuli-responsive biomolecules represent a major advance toward implementation of full synaptic functionality in neuromorphic hardware.
Two-terminal memory elements, or memelements, capable of co-locating signal processing and memory via history-dependent reconfigurability at the nanoscale are vital for next-generation computing materials striving to match the brain’s efficiency and flexible cognitive capabilities. While memory resistors, or memristors, have been widely reported, other types of memelements remain underexplored or undiscovered. Here we report the first example of a volatile, voltage-controlled memcapacitor in which capacitive memory arises from reversible and hysteretic geometrical changes in a lipid bilayer that mimics the composition and structure of biomembranes. We demonstrate that the nonlinear dynamics and memory are governed by two implicitly-coupled, voltage-dependent state variables—membrane radius and thickness. Further, our system is capable of tuneable signal processing and learning via synapse-like, short-term capacitive plasticity. These findings will accelerate the development of low-energy, biomolecular neuromorphic memelements, which, in turn, could also serve as models to study capacitive memory and signal processing in neuronal membranes.
Providing AI platforms with perceptual capabilities at low energy cost is imperative to making them more human-like. This goal is contingent on developing stimuliresponsive materials that closely emulate diverse synaptic functions needed to enhance machine learning applications. Here a biomolecular device is reported, consisting of an insulating lipid membrane doped with voltage-activated, ion channel-forming monazomycin (Mz) species, that display diode-like current−voltage characteristics and emulate short-term facilitation-then-depression on time scales relevant to habituation in human sensory systems. Subjected to a slow train of voltage pulses, devices show only facilitation upon Mz channel formation. At higher pulse rates, faster facilitation creates later depression upon Mz inactivation. The device is integrated as a biomolecular synapse in an organic−inorganic hybrid afferent model to demonstrate its ability to process sensory inputs and mimic bidirectional retinal adaptation and sensitization. These findings highlight the value of instilling complex synaptic plasticity into engineered systems, which is useful for adaptive neuroprosthetics and biosignal processing.
In neuromorphic computing, memristors (or “memory resistors”) have been primarily studied as key elements in artificial synapse implementations, where the memristor provides a variable weight with intrinsic long-term memory capabilities, based on its modifiable resistive-switching characteristics. Here, we demonstrate an efficient methodology for simulating resistive-switching of HfO2 memristors within Synopsys TCAD Sentaurus—a well established, versatile framework for electronic device simulation, visualization and modeling. Kinetic Monte Carlo is used to model the temporal dynamics of filament formation and rupture wherein additional band-to-trap electronic transitions are included to account for polaronic effects due to strong electron-lattice coupling in HfO2. The conductive filament is modeled as oxygen vacancies which behave as electron traps as opposed to ionized donors, consistent with recent experimental data showing p-type conductivity in HfOx films having high oxygen vacancy concentrations and ab-initio calculations showing the increased thermodynamic stability of neutral and charged oxygen vacancies under conditions of electron injection. Pulsed IV characteristics are obtained by inputting the dynamic state of the system—which consists of oxygen ions, unoccupied oxygen vacancies, and occupied oxygen vacancies at various positions—into Synopsis TCAD Sentaurus for quasi-static simulations. This allows direct visualization of filament electrostatics as well as the implementation of a nonlocal, trap-assisted-tunneling model to estimate current-voltage characteristics during switching. The model utilizes effective masses and work functions of the top and bottom electrodes as additional parameters influencing filament dynamics. Together, this approach can be used to provide valuable device- and circuit-level insight, such as forming voltage, resistance levels and success rates of programming operations, as we demonstrate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.