Self-Induced Light Emission in Solid-State Memristors Replicates Neuronal Biophotons.

Key neuronal functions have been successfully replicated in various hardware systems. Noticeable examples are neuronal networks constructed from memristors, which emulate complex electrochemical biological dynamics such as the efficacy and plasticity of a neuron. Neurons are highly active cells, communicating with chemical and electrical stimuli, but also emit light. These so-called biophotons are suspected to be a complementary vehicle to transport information across the brain. Here, we show that a memristor also releases photons during its operation akin to the production of neuronal light. Critical attributes of biophotons, such as self-generation, stochasticity, spectral coverage, sparsity, and correlation with the neuron's electrical activity, are replicated by our solid-state approach. Importantly, our time-resolved analysis of the correlated current transport and photon activity shows that emission takes place within a nanometer-sized active area and relies on electrically induced single-to-few active electroluminescent centers excited with moderate voltage (<3 V). Our findings further extend the emulating capability of a memristor to encompass neuronal optical activity and allow to construct memristive atomic-scale devices capable of handling simultaneously electrons and photons as information carriers.

Single neuromorphic memristor closely emulates multiple synaptic mechanisms for energy efficient neural networks.

Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or learning-to-learn. The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.

Phase-Change Memory from Molecular Tellurides.

Phase-change memory (PCM) is an emerging memory technology based on the resistance contrast between the crystalline and amorphous states of a material. Further development and realization of PCM as a mainstream memory technology rely on innovative materials and inexpensive fabrication methods. Here, we propose a generalizable and scalable solution-processing approach to synthesize phase-change telluride inks in order to meet demands for high-throughput material screening, increased energy efficiency, and advanced device architectures. Bulk tellurides, such as Sb2Te3, GeTe, Sc2Te3, and TiTe2, are dissolved and purified to obtain inks of molecular metal telluride complexes. This allowed us to unlock a wide range of solution-processed ternary tellurides by the simple mixing of binary inks. We demonstrate accurate and quantitative composition control, including prototype materials (Ge-Sb-Te) and emerging rare-earth-metal telluride-doped materials (Sc-Sb-Te). Spin-coating and annealing convert ink formulations into high-quality, phase-pure telluride films with preferred orientation along the (00l) direction. Deposition engineering of liquid tellurides enables thickness-tunable films, infilling of nanoscale vias, and film preparation on flexible substrates. Finally, we demonstrate cyclable and non-volatile prototype memory devices, achieving performance indicators such as resistance contrast and low reset energy on par with state-of-the-art sputtered PCM layers.

Atomic scale memristive photon source.

Memristive devices are an emerging new type of devices operating at the scale of a few or even single atoms. They are currently used as storage elements and are investigated for performing in-memory and neuromorphic computing. Amongst these devices, Ag/amorphous-SiOx/Pt memristors are among the most studied systems, with the electrically induced filament growth and dynamics being thoroughly investigated both theoretically and experimentally. In this paper, we report the observation of a novel feature in these devices: The appearance of new photoluminescent centers in SiOx upon memristive switching, and photon emission correlated with the conductance changes. This observation might pave the way towards an intrinsically memristive atomic scale light source with applications in neural networks, optical interconnects, and quantum communication.