Potential and challenges of computing with molecular materials.

We are at an inflection point in computing where traditional technologies are incapable of keeping up with the demands of exploding data collection and artificial intelligence. This challenge demands a leap to a new platform as transformative as the digital silicon revolution. Over the past 30 years molecular materials for computing have generated great excitement but continually fallen short of performance and reliability requirements. However, recent reports indicate that those historical limitations may have been resolved. Here we assess the current state of computing with molecular-based materials, especially using transition metal complexes of redox active ligands, in the context of neuromorphic computing. We describe two complementary research paths necessary to determine whether molecular materials can be the basis of a new computing technology: continued exploration of the molecular electronic properties that enable computation and, equally important, the process development for on-chip integration of molecular materials.

Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems.

Neurobiological circuits containing synapses can process signals while learning concurrently in real time. Before an artificial neural network (ANN) can execute a signal-processing program, it must first be programmed by humans or trained with respect to a large and defined data set during learning processes, resulting in significant latency, high power consumption, and poor adaptability to unpredictable changing environments. In this work, a crossbar circuit of synaptic resistors (synstors) is reported, each synstor integrating a Si channel with an Al oxide memory layer and Ti silicide Schottky contacts. Individual synstors are characterized and analyzed to understand their concurrent signal-processing and learning abilities. Without any prior training, synstor circuits concurrently execute signal processing and learning in real time to fly drones toward a target position in an aerodynamically changing environment faster than human controllers, and with learning speed, performance, power consumption, and adaptability to the environment significantly superior to an ANN running on computers. The synstor circuit provides a path to establish power-efficient intelligent systems with real-time learning and adaptability in the capriciously mutable real world.

Energy and Space Efficient Parallel Adder Using Molecular Memristors.

A breakthrough in in-memory computing technologies hinges on the development of appropriate material platforms that can overcome their existing limitations, such as larger than optimal footprint and multiple serial computational steps, with potential accumulation of errors. Using a molecular switching element with multiple non-monotonic and deterministic transitions, the device count and the number of computational steps can be substantially reduced. With molecular materials, however, the realization of a reliable and robust platform is an unattained goal for decades. Here, crossbar arrays with up to 64 molecular memristors are fabricated to experimentally demonstrate 8-bit serial and 4-bit parallel adders that operate for thousands of measurement cycles with an estimated error probability of 10-16 . For performance benchmarking, a 32-bit parallel adder is designed and simulated with 268 million inputs including contributions from the peripheral circuitry showing a 47× higher energy efficiency, 93× faster operation, and 9% of the footprint, leading to 4390 times improved energy-delay product compared to a special purpose complementary metal-oxide-semiconductor (CMOS)-based multicore adder.

Electro-Thermal Characterization of Dynamical VO2 Memristors via Local Activity Modeling.

Translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO2 /SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.

Decision trees within a molecular memristor.

Profuse dendritic-synaptic interconnections among neurons in the neocortex embed intricate logic structures enabling sophisticated decision-making that vastly outperforms any artificial electronic analogues1-3. The physical complexity is far beyond existing circuit fabrication technologies: moreover, the network in a brain is dynamically reconfigurable, which provides flexibility and adaptability to changing environments4-6. In contrast, state-of-the-art semiconductor logic circuits are based on threshold switches that are hard-wired to perform predefined logic functions. To advance the performance of logic circuits, we are re-imagining fundamental electronic circuit elements by expressing complex logic in nanometre-scale material properties. Here we use voltage-driven conditional logic interconnectivity among five distinct molecular redox states of a metal-organic complex to embed a 'thicket' of decision trees (composed of multiple if-then-else conditional statements) having 71 nodes within a single memristor. The resultant current-voltage characteristic of this molecular memristor (a 'memory resistor', a globally passive resistive-switch circuit element that axiomatically complements the set of capacitor, inductor and resistor) exhibits eight recurrent and history-dependent non-volatile switching transitions between two conductance levels in a single sweep cycle. The identity of each molecular redox state was determined with in situ Raman spectroscopy and confirmed by quantum chemical calculations, revealing the electron transport mechanism. Using simple circuits of only these elements, we experimentally demonstrate dynamically reconfigurable, commutative and non-commutative stateful logic in multivariable decision trees that execute in a single time step and can, for example, be applied as local intelligence in edge computing7-9.

Third-order nanocircuit elements for neuromorphic engineering.

Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics1-4. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes)5, but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element6-8. Using both experiments and modelling, here we show how multiple electrophysical processes-including Mott transition dynamics-form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models.

Nanometer-Scale Uniform Conductance Switching in Molecular Memristors.

One common challenge highlighted in almost every review article on organic resistive memory is the lack of areal switching uniformity. This, in fact, is a puzzle because a molecular switching mechanism should ideally be isotropic and produce homogeneous current switching free from electroforming. Such a demonstration, however, remains elusive to date. The reports attempting to characterize a nanoscopic picture of switching in molecular films show random current spikes, just opposite to the expectation. Here, this longstanding conundrum is resolved by demonstrating 100% spatially homogeneous current switching (driven by molecular redox) in memristors based on Ru-complexes of azo-aromatic ligands. Through a concurrent nanoscopic spatial mapping using conductive atomic force microscopy and in operando tip-enhanced Raman spectroscopy (both with resolution <7 nm), it is shown that molecular switching in the films is uniform from hundreds of micrometers down to the nanoscale and that conductance value exactly correlates with spectroscopically determined molecular redox states. This provides a deterministic molecular route to obtain spatially homogeneous, forming-free switching that can conceivably overcome the chronic problems of robustness, consistency, reproducibility, and scalability in organic memristors.

Charge disproportionate molecular redox for discrete memristive and memcapacitive switching.

Electronic symmetry breaking by charge disproportionation results in multifaceted changes in the electronic, magnetic and optical properties of a material, triggering ferroelectricity, metal/insulator transition and colossal magnetoresistance. Yet, charge disproportionation lacks technological relevance because it occurs only under specific physical conditions of high or low temperature or high pressure. Here we demonstrate a voltage-triggered charge disproportionation in thin molecular films of a metal-organic complex occurring in ambient conditions. This provides a technologically relevant molecular route for simultaneous realization of a ternary memristor and a binary memcapacitor, scalable down to a device area of 60 nm2. Supported by mathematical modelling, our results establish that multiple memristive states can be functionally non-volatile, yet discrete-a combination perceived as theoretically prohibited. Our device could be used as a binary or ternary memristor, a binary memcapacitor or both concomitantly, and unlike the existing 'continuous state' memristors, its discrete states are optimal for high-density, ultra-low-energy digital computing.

Fast Spiking of a Mott VO2-Carbon Nanotube Composite Device.

The recent surge of interest in brain-inspired computing and power-efficient electronics has dramatically bolstered development of computation and communication using neuron-like spiking signals. Devices that can produce rapid and energy-efficient spiking could significantly advance these applications. Here we demonstrate direct current or voltage-driven periodic spiking with sub-20 ns pulse widths from a single device composed of a thin VO2 film with a metallic carbon nanotube as a nanoscale heater, without using an external capacitor. Compared with VO2-only devices, adding the nanotube heater dramatically decreases the transient duration and pulse energy, and increases the spiking frequency, by up to 3 orders of magnitude. This is caused by heating and cooling of the VO2 across its insulator-metal transition being localized to a nanoscale conduction channel in an otherwise bulk medium. This result provides an important component of energy-efficient neuromorphic computing systems and a lithography-free technique for energy-scaling of electronic devices that operate via bulk mechanisms.

Author Correction: Dynamical nonlinear memory capacitance in biomimetic membranes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Dynamical nonlinear memory capacitance in biomimetic membranes.

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.

Summary of the Faraday Discussion on New memory paradigms: memristive phenomena and neuromorphic applications.

The Faraday Discussion on New memory paradigms: memristive phenomena and neuromorphic systems was held from October 15-17 on the campus of the Rheinisch-Westfälische Technische Hochschule Aachen University, or RWTH Aachen University, under the auspices of the Royal Society of Chemistry. The Discussion brought together over 100 experts and students from over 20 different countries to present, question, and debate the latest breakthroughs in a field that is exhibiting dramatic growth. There were a large number of excellent oral presentations and posters covering the topics of the Discussion, and the conversations among the participants were lively and informative. The first day and a half encompassed the experimental characterization and analysis of memristors for which the primary resistive switching mechanisms involved electrochemical metallization, chemical valence transformation, or thermodynamic phase change, which illustrated the diversity of the physical properties that can be harnessed for building memristive systems. The final half-day of the Discussion was devoted to synaptic and neuromorphic functions of memristors, which have been growing explosively in the past few years and demonstrate that resistive switching is important for much more than a non-volatile memory, but can also be utilized for learning and computation. The high quality of the presentations and discussions in this section demonstrated that this new research area has been solidly launched and we can expect significant advancements and real applications within a few years as new discoveries from biology are incorporated into neuromorphic hardware. I will not attempt to recapitulate the content of all of the sessions here, since the papers collected for this Faraday Discussions volume speak more eloquently for themselves than I can affect. What I hope to accomplish is to point out a few general concepts that arose when considering multiple papers together, especially during the discussions, and point out a particularly novel concept that deserves special attention.

Capacitive neural network with neuro-transistors.

Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with "leaky integrate-and-fire" dynamics augmented by a signal gain on the output. Paired with non-volatile pseudo-memcapacitive synapses, a Hebbian-like learning mechanism is implemented in a capacitive switching network, leading to the observed associative learning. A prototypical fully integrated capacitive neural network is built and used to classify inputs of signals.