The application of ferroelectric materials (i.e. solids that exhibit spontaneous electric polarisation) in solar cells has a long and controversial history. This includes the first observations of the anomalous photovoltaic effect (APE) and the bulk photovoltaic effect (BPE). The recent successful application of inorganic and hybrid perovskite structured materials (e.g. BiFeO3, CsSnI3, CH3NH3PbI3) in solar cells emphasises that polar semiconductors can be used in conventional photovoltaic architectures. We review developments in this field, with a particular emphasis on the materials known to display the APE/BPE (e.g. ZnS, CdTe, SbSI), and the theoretical explanation. Critical analysis is complemented with first-principles calculation of the underlying electronic structure. In addition to discussing the implications of a ferroelectric absorber layer, and the solid state theory of polarisation (Berry phase analysis), design principles and opportunities for high-efficiency ferroelectric photovoltaics are presented.

/pdf/ferroelectric-materials-for-solar-energy-conversion-2y5gwkw07q.pdf

Ferroelectric Materials for Solar Energy Conversion: Photoferroics Revisited

The continued growth in the demand of data storage and processing has spurred the development of high-performance storage technologies and brain-inspired neuromorphic hardware. Semiconductor quantum dots (QDs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-based manufactured technologies. Here, we focus on the development of nonvolatile memories and neuromorphic computing systems based on QD thin-film solids. We introduce recent advances of QDs and highlight their unique electrical and optical features for designing future electronic devices. We also discuss the advantageous traits of QDs for novel and optimized memory techniques in both conventional flash memories and emerging memristors. Then, we review recent advances in QD-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. Finally, we highlight major challenges for commercial translation and consider future directions for the postsilicon era.

Semiconductor Quantum Dots for Memories and Neuromorphic Computing Systems

Optoelectronic synapses integrating synaptic and optical-sensing functions exhibit large advantages in neuromorphic computing for visual information processing and complex learning, recognition, and memory in an energy-efficient way. However, electric stimulation is still essential for existing optoelectronic synapses to realize bidirectional weight-updating, restricting the processing speed, bandwidth, and integration density of the devices. Herein, a two-terminal optical synapse based on a wafer-scale pyrenyl graphdiyne/graphene/PbS quantum dot heterostructure is proposed that can emulate both the excitatory and inhibitory synaptic behaviors in an optical pathway. The simple device architecture and low-dimensional features of the heterostructure endow the optical synapse with robust flexibility for wearable electronics. This optical synapse features a linear and symmetric conductance-update trajectory with numerous conductance states and low noise, which facilitates the demonstration of accurate and effective pattern recognition with a strong fault-tolerant capability even at bending states. A series of logic functions and associative learning capabilities have been demonstrated by the optical synapses in optical pathways, significantly enhancing the information processing capability for neuromorphic computing. Moreover, an integrated visible information sensing memory processing system based on the optical synapse array is constructed to perform real-time detection, in situ image memorization, and distinction tasks. This work is an important step toward the development of optogenetics-inspired neuromorphic computing and adaptive parallel processing networks for wearable electronics.

Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing.

The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike-based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain-like computing.

Neuromorphic Engineering: From Biological to Spike‐Based Hardware Nervous Systems

Imprinting vision as memory is a core attribute of human cognitive learning. Fundamental to artificial intelligence systems are bioinspired neuromorphic vision components for the visible and invisible segments of the electromagnetic spectrum. Realization of a single imaging unit with a combination of in-built memory and signal processing capability is imperative to deploy efficient brain-like vision systems. However, the lack of a platform that can be fully controlled by light without the need to apply alternating polarity electric signals has hampered this technological advance. Here, a neuromorphic imaging element based on a fully light-modulated 2D semiconductor in a simple reconfigurable phototransistor structure is presented. This standalone device exhibits inherent characteristics that enable neuromorphic image pre-processing and recognition. Fundamentally, the unique photoresponse induced by oxidation-related defects in 2D black phosphorus (BP) is exploited to achieve visual memory, wavelength-selective multibit programming, and erasing functions, which allow in-pixel image pre-processing. Furthermore, all-optically driven neuromorphic computation is demonstrated by machine learning to classify numbers and recognize images with an accuracy of over 90%. The devices provide a promising approach toward neurorobotics, human-machine interaction technologies, and scalable bionic systems with visual data storage/buffering and processing.

Fully Light-Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus.

Fully photon modulated heterostructure for neuromorphic computing

A self-powered artificial retina perception system for image preprocessing based on photovoltaic devices and memristive arrays

Ferroelectric polymers for non‐volatile memory devices: a review

https://onlinelibrary.wiley.com/doi/pdf/10.1002/eom2.12099

Huilin Li

Papers

Fully photon modulated heterostructure for neuromorphic computing

A self-powered artificial retina perception system for image preprocessing based on photovoltaic devices and memristive arrays

Ferroelectric polymers for non‐volatile memory devices: a review

Multifunctional CNT:TiO2 additives in spiro-OMeTAD layer for highly efficient and stable perovskite solar cells

Photoferroelectric perovskite solar cells: Principles, advances and insights