Robert C. Dynes

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.

Highly oriented pyrolytic graphite may be the only known monatomic crystal with the ability to host naturally formed moiré patterns on its cleaved surfaces, which are coherent over micrometer scales and with discrete sets of twist angles of fixed periodicity. Such an aspect is in marked contrast to twisted bilayer graphene and other multilayered systems, where the long-range coherence of the moiré is not easily maintained due to twist angle disorder. We investigate the electronic and mechanical response of coherent graphite moiré patterns through inducing external strain from scanning tunneling microscopy tip-induced deformation. Consequently, unique anisotropic mechanical characteristics are revealed. For example, a lateral widening of one-dimensional domain walls (DWs) bridging Bernal (ABA) and rhombohedral (ABC) stacking domains (A, B, and C refer to the atomic layer positioning), was indicated. Further, in situ tunneling spectroscopy as a function of the deformation indicated a tendency towards increased electrical conductance, which may be associated with a higher density of electronic states, and the consequent flattening of the electronic energy band dispersion. Such features were probed across the DWs, with implications for strain-induced electronic modulation of the moiré characteristics.

A methodology to test the interlayer bonding strength of two-dimensional (2D) surfaces and associated one (1D)- and two (2D)- dimensional surface defects using scanning tunneling microscope tip-induced deformation, is demonstrated. Surface elastic deformation characteristics of soft 2D monatomic sheets of graphene and graphite in contrast to NbSe2 indicates related association with the underlying local bonding configurations. Surface deformation of 2D graphitic moire patterns reveal the inter-layer van der Waals strength varying across its domains. These results help in the understanding of the comparable interlayer bonding strength of 1D grain boundary as well as the grains. Anomalous phenomena related to probing 2D materials at small gap distances as a function of strain is discussed.

A methodology to test the interlayer bonding strength of two-dimensional (2D) surfaces and associated one (1D)- and two (2D)- dimensional surface defects using scanning tunneling microscope tip-induced deformation, is demonstrated. Surface elastic deformation characteristics of soft 2D monatomic sheets of graphene and graphite in contrast to NbSe₂ indicates related association with the underlying local bonding configurations. Surface deformation of 2D graphitic moiré patterns reveal the inter-layer van der Waals strength varying across its domains. These results help in the understanding of the comparable interlayer bonding strength of 1D grain boundary as well as the grains. Anomalous phenomena related to probing 2D materials at small gap distances as a function of strain is discussed.

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

In superconductors, magnetic fields are quantized into discrete fluxons (flux quanta Φ ₀ ), made of microscopic circulating supercurrents. We introduce a multiterminal synapse network comprising a disordered array of superconducting loops with Josephson junctions. The loops can trap fluxons defining memory, while the junctions allow their movement between loops. Dynamics of fluxons through such a disordered system through a complex reconfigurable energy landscape represents brain-like spiking information flow. In this work, we experimentally demonstrate a three-loop network using YBa ₂ Cu ₃ O _{7 − δ} -based superconducting loops and Josephson junctions, which exhibit stable memory configurations of trapped flux in loops that determine the rate of flow of fluxons through synaptic connections. The memory states are, in turn, affected by the applied input signals but can also be externally configured electrically through control current/feedback terminals. These results establish a previously unexplored, biologically similar architectural approach to neuromorphic computing that is scalable while dissipating energy of atto Joules/spike.

Atomic modulations of two-dimensional materials using scanning tunneling microscope (STM) tip-induced forces modifies their mechanical and electrical properties. In situ topographic and spectroscopic probing through electrical tunneling has been used for straining sheets of graphite, monolayer graphene and NbSe2. The findings also resolve a thirty-five-year-old controversy involving numerous proposed models to explain the source of anomalously high measured atomic amplitudes (of up to 24 Angstroms, expected 0.2 Angstroms) from atomic corrugation on graphite surfaces. Our findings attributes the anomaly to surface elastic deformation characteristics of soft 2D monatomic sheets of graphene and graphite in contrast to NbSe2 which is associated with their local bonding configurations. The tip-induced deformations are shown to induce controlled strain on the material surface atomically and it offers a new way for strain engineering. Topographic deformation of formed graphitic Moire patterns reveals the inter-layer van der Waals (vdW) strength varying across its domains. In-situ tunneling spectroscopy associated with straining of the Moire domains reveals electronic flat band formation controllably thereby creating a platform for many-body correlations. The paper cautions anomalous observations when probing 2D materials at small gap distances with their strain induced effects and provides guidelines to exploit or avoid this effect.