Many-Body Molecular Interactions in a Memristor

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.

Linear symmetric self-selecting 14-bit kinetic molecular memristors

Artificial Intelligence (AI) is the domain of large resource-intensive data centres that limit access to a small community of developers1,2. Neuromorphic hardware promises greatly improved space and energy efficiency for AI but is presently only capable of low-accuracy operations, such as inferencing in neural networks3-5. Core computing tasks of signal processing, neural network training and natural language processing demand far higher computing resolution, beyond that of individual neuromorphic circuit elements6-8. Here we introduce an analog molecular memristor based on a Ru-complex of an azo-aromatic ligand with 14-bit resolution. Precise kinetic control over a transition between two thermodynamically stable molecular electronic states facilitates 16,520 distinct analog conductance levels, which can be linearly and symmetrically updated or written individually in one time step, substantially simplifying the weight update procedure over existing neuromorphic platforms3. The circuit elements are unidirectional, facilitating a selector-less 64 × 64 crossbar-based dot-product engine that enables vector-matrix multiplication, including Fourier transform, in a single time step. We achieved more than 73 dB signal-to-noise-ratio, four orders of magnitude improvement over the state-of-the-art methods9-11, while consuming 460× less energy than digital computers12,13. Accelerators leveraging these molecular crossbars could transform neuromorphic computing, extending it beyond niche applications and augmenting the core of digital electronics from the cloud to the edge12,13.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

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.