Ferroelectric-based synapses and neurons for neuromorphic computing

Abstract

The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems' power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technology for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.

Synaptic behaviour in ferroelectric epitaxial rhombohedral Hf0.5Zr0.5O2 thin films

“…Ferroelectricity in doped hafnia is widely attributed to a polar orthorhombic phase (o-phase, space group Pca2 1 ) [15]. There have been various studies showing promising neuromorphic behaviour in hafnia-based ferroelectrics [16][17][18][19][20][21][22][23]. This has been demonstrated in the form of ferroelectric capacitors (FC), ferroelectric field-effect transistors (FeFET), and ferroelectric tunnel junctions (FTJ).…”

Neuromorphic devices based on fluorite‐structured ferroelectrics

“…185 Owing to the presence of multi-domains in ferroelectric materials in large-scale FeFETs with channel length >100 nm, the polarization states can be tuned by the gate pulses, which is followed by a gradual changes in the threshold voltage (V T ) and channel conductance under an external electric field. 95,186,187 Accurate control of the polarization state and subsequent channel conductance for the synaptic device application can be achieved by utilizing several subsequent voltage pulses with an appropriate amplitude and width. Figure 4B shows a comparison of the device-level performances of FTJs and FeFETs.…”

Acoustic scene analysis using analog spiking neural network

“…The mixed implementation has significant benefits in terms of energy efficiency with proper design parameters such as voltages and currents [50]. The SNN can be implemented in a resistive crossbar network (RCN) fashion using a memristor composed of CMOS or ferroelectric devices to store synaptic weights [21,24,25,[52][53][54]56]. Multi-level analog weights have been implemented using a memristor [52,53,61].…”