Efficient Deep Neural Network Accelerator Using Controlled Ferroelectric Domain Dynamics

The current work reports an efficient deep neural network (DNN) accelerator where synaptic weight elements are controlled by ferroelectric domain dynamics. An integrated device-to-algorithm framework for benchmarking novel synaptic devices is used. In P(VDF-TrFE) based ferroelectric tunnel junctions, analog conductance states are measured using a custom pulsing protocol and associated custom circuits and array architectures for DNN training is simulated. Our results show precise control of polarization switching dynamics in multi-domain, polycrystalline ferroelectric thin films can produce considerable weight update linearity in metal-ferroelectric-semiconductor (MFS) tunnel junctions. Ultrafast switching and low junction current in these devices offer extremely energy efficient operation. Through an integrated platform of hardware development, characterization and modelling, we predict the available conductance range where linearity is expected under identical potentiating and depressing pulses for efficient DNN training and inference tasks. As an example, an analog crossbar based DNN accelerator with MFS junctions as synaptic weight elements showed ~ 93% training accuracy on large MNIST handwritten digit dataset while for cropped images, a 95% accuracy is achieved. One observed challenge is rather limited dynamic conductance range while operating under identical potentiating and depressing pulses below 1V. Investigation is underway for improving the dynamic conductance range without losing the weight update linearity

Development of Modeling Approaches for A Molecular Level Understanding of Ligand-Lanthanide-Water Systems

Lanthanides (Ln) are a subset of rare earth elements (REEs) that are essential components in electric vehicles, smart phones, and wind turbines. Current REE recovery processes are time intensive and produce hazardous wastes. Developing sustainable recovery processes require new techniques that are economical and less wasteful, such as affinity-based extraction and separations. Enabling affinity-based recovery requires use of ligands designed to bind strongly and selectively to lanthanides. Developing design rules to make such ligands requires an understanding of the coordination environment of lanthanide-ligand complexes. In this work, we begin to develop a molecular level understanding of aqueous lanthanide-ligand systems with the ligand ethylenediaminetetraacetic acid (EDTA). We develop computational models of aqueous EDTA complexes of lanthanides La, Ce, Pr and Nd, informed by molecular dynamics (MD) and density functional theory (DFT). These models are developed so they can be used as the required reference structures for experimental techniques such as extended x-ray absorption fine structure (EXAFS) that provide structural information about metallic complexes. Our results suggest shortcomings in MD structures and indicate that DFT optimized structures prove to be reasonable models for aqueous Ln-EDTA complexes. These DFT structures are used to generate theoretical spectra that are fit against experimental EXAFS spectra to uncover information about bond distances and organization in aqueous Ln-EDTA complexes. Overall, this work contributes to developing a method to combine theoretical data with experimental data to understand the coordination environment around aqueous phase ligand-lanthanide systems, which is a key step towards designing ligands for sustainable selective REE separations