Neuromorphic computing is considered a promising method for resolving the traditional von Neumann bottleneck. Natural biomaterial-based artificial synapses are popular units for constructing neuromorphic computing systems while suffering from poor linearity and limited conduction states. In this work, a AgNO doped iota-carrageenan (ι-car) based memristor is proposed to resolve the non-linear limitation. The memristor presents linear conductance tuning with a higher endurance (∼10), more enriched conduction states (>2000), and much lower power consumption (∼3.6 μW) than previously reported biomaterial-based analog memristors. AgNO is doped to ι-car to suppress the formation of Ag filaments, thereby eliminating uneven Joule heating. Using deep learning of hand-written digits as an application, a doping-enhanced recognition accuracy (93.8%) is achieved, close to that of an ideal synaptic device (95.7%). This work verifies the feasibility of using biopolymers for future high-performance computational and wearable/implantable electronic applications.
Memristors and magnetic tunnel junctions are showing great potential in data storage and computing applications. A magnetoelectrically coupled memristor utilizing electron spin and electric field-induced ion migration can facilitate their operation, uncover new phenomena, and expand applications. In this study, devices consisting of Pt/(LaCoO/SrTiO)/LaCoO/Nb:SrTiO (Pt/(LCO/STO)/LCO/NSTO) are engineered using pulsed laser deposition to form the LCO/STO superlattice layer, with Pt and NSTO serving as the top and bottom electrodes, respectively.
Domain walls are quasi-one-dimensional topological defects in ferroic materials, which can harbor emergent functionalities. In the case of ferroelectric domain wall (FEDW) devices, an exciting frontier has emerged: memristor-based information storage and processing approaches. Memristor solid-state FEDW devices presented thus far, however predominantly utilize a complex network of domain walls to achieve the desired regulation of density and charge state.
A synaptic memristor using 2D ferroelectric junctions is a promising candidate for future neuromorphic computing with ultra-low power consumption, parallel computing, and adaptive scalable computing technologies. However, its utilization is restricted due to the limited operational voltage memory window and low on/off current (I) ratio of the memristor devices. Here, it is demonstrated that synaptic operations of 2D InSe ferroelectric junctions in a planar memristor architecture can reach a voltage memory window as high as 16 V (±8 V) and I ratio of 10, significantly higher than the current literature values.
Physical unclonable functions (PUFs), often referred to as digital fingerprints, are emerging as critical elements in enhancing hardware security and encryption. While significant progress has been made in developing optical and memory-based PUFs, integrating reconfigurability with sensitivity to circularly polarized light (CPL) remains largely unexplored. Here, we present a chiroptical synaptic memristor (CSM) as a reconfigurable PUF, leveraging a two-dimensional organic-inorganic halide chiral perovskite.
Multibit/analog artificial synapses are in demand for neuromorphic computing systems. A problem hindering the utilization of memristive artificial synapses in commercial neuromorphic systems is the rigidity of their functional parameters, plasticity in particular. Here, we report fabricating polycrystalline rutile-based memristive memory segments with Ti/poly-TiO/Ti structures featuring multibit/analog storage and the first use of a tunable DC-biasing for synaptic plasticity adjustment from short- to long-term.
