Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision

Abstract

Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr0.2Ti0.8)O3 layer, show not only multiple nonvolatile levels but also a sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. It is demonstrated that an in situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is available in our FE-PS-NET hardware. The FE-PS-NET hardware is faultlessly competent for real-time image processing functionalities, including binary pattern classification with an accuracy of 100% and edge detection with an F-Measure of 95.2%. This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.

Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification

“…For the classification to these letters, as depicted in Fig. 5e, a simple neural network is built by multiply-accumulate circuits (MAC) 47,48 , where structure of circuit is shown in Supplementary Fig. S14, since the drain current is linear in the measured range (−20V to 20 V) of drain voltage under the light illumination (Supplementary Fig.…”

Reconfigurable, non-volatile neuromorphic photovoltaics

“…To address this issue, researchers have developed retina-inspired devices based on bulk materials and two-dimensional (2D) materials, such as variable-sensitivity photodetectors (VSDP) 6, 7 , dual-gate photodiodes (DGP) 8,9 , two-terminal photo-memories (TPM) [10][11][12][13][14] , and gate-tunable vision sensors (GVS) [15][16][17][18][19][20][21][22] . However, these neural network sensors often suffer from bias-dependent dark current with high power consumption (VSDP), volatile photocurrent for neural network (TPM), or complicated preparation process for integration and low photoresponsivity (DGP, GVS).…”

Back-End, CMOS-Compatible Ferroelectric FinFET for Synaptic Weights

“…The recent discovery of the ferroelectric properties in hafnia composites (Böscke et al, 2011), a material already present in CMOS technology, has attracted further scientific interest in the field of neuromorphic hardware based on ferroelectrics. Three main classes of devices exploiting ferroelectricity for synaptic as well as neuronal functionalities were demonstrated in the past: the twoterminal Ferroelectric Tunneling Junctions (FTJs) (Ambriz-Vargas et al, 2017;Tian and Toriumi, 2017;Chen et al, 2018a;Goh and Jeon, 2018;Yu et al, 2021), the threeterminal Ferroelectric Field-Effect Transistors (FeFETs) (Mulaosmanovic et al, 2017;Sharma et al, 2017;Krivokapic et al, 2018;Zeng et al, 2018;Mo et al, 2019) and the twoterminal Ferroelectric Photovoltaic (FePv) synapses (Cheng et al, 2020;Cui et al, 2021). Although, both FTJs and FeFETs have been extensively investigated recently, showing large dynamic ranges, low energy dissipation, and synaptic functions including short and long term plasticity as well as Spike-Timing-Dependent Plasticity (STDP) (Nishitani et al, 2012;Boyn et al, 2017;Chen et al, 2018a;Guo et al, 2018;Majumdar et al, 2019;Li et al, 2020) the FePv devices, based on the polarization control of the photovoltaic behavior that exploit the photoresponsivity as synaptic weight, were used for binary data storage (Guo et al, 2013) and recently as prototype synapse (Cheng et al, 2020).…”