Abstract
Ferroelectric materials are promising candidates for synaptic weight elements in neural network hardware because of their nonvolatile multilevel memory effect. This feature is crucial for their use in mobile applications such as inference when vector matrix multiplication is performed during portable artificial intelligence service. In addition, the adaptive learning effect in ferroelectric polarization has gained considerable research attention for reducing the CMOS circuit overhead of an integrator and amplifier with an activation function. In spite of their potential for a weight and a neuron, material issues have been pointed out for commercialization in conjunction with CMOS processing and device structures. Herein, we review ferroelectric synaptic weights and neurons from the viewpoint of materials in relation to device operation, along with discussions and suggestions for improvement. Moreover, we discuss the reliability of HfO2 as an emerging material and suggest methods to overcome the scaling issue of ferroelectrics.
Highlights
The use of ferroelectric thin films was recommended for the connection of elements according to the need of the development of integrated circuit devices that directly facilitate the use of artificial neural network (ANN) as analog circuits in 1989.1 The ferroelectric synaptic weight for a neural network was demonstrated by a single bit memory effect or a multilevel memory effect[1,2,3,4,5,6,7,8,9,10] in the form of a ferroelectric capacitor,[1,2] ferroelectric field effect transistor (FeFET), and ferroelectric tunnel junction (FTJ).[11,12,13]
Typical wave forms to operate ferroelectric weight are well summarized in Ref. 16, where amplitude and pulse width modes are shown for ANN and prespike and postspike wave forms are shown for spiketiming-dependent plasticity (STDP)
In ANN application, ferroelectrics are competitive as a synaptic weight, especially the 1T-1C type, because of their nonvolatile multilevel property, as mentioned earlier
Summary
The use of ferroelectric thin films was recommended for the connection of elements according to the need of the development of integrated circuit devices that directly facilitate the use of artificial neural network (ANN) as analog circuits in 1989.1 The ferroelectric synaptic weight for a neural network was demonstrated by a single bit memory effect or a multilevel memory effect[1,2,3,4,5,6,7,8,9,10] in the form of a ferroelectric capacitor,[1,2] ferroelectric field effect transistor (FeFET), and ferroelectric tunnel junction (FTJ).[11,12,13] the ferroelectric weight was reported to be controlled by spiketiming-dependent plasticity (STDP).[14,15,16,17] The ferroelectric neuron, which generates a pulse that activates a new input, was demonstrated on the basis of an adaptive learning function.[18,19,20,21] Neurons (preand post-) connecting via a three-terminal FeFET made of a ferroelectric thin-film transistor (TFT) was studied.[22]. Ferroelectric weights are applied to both ANN and STDP with different operation. Typical wave forms to operate ferroelectric weight are well summarized in Ref. 16, where amplitude and pulse width modes are shown for ANN and prespike and postspike wave forms are shown for STDP. It is similar to that of ferroelectric memories because the memory can be directly applied to synaptic weight. This article will mainly focus on ANN when it comes to ferroelectric weight. We present several issues and limitations of ferroelectrics, mainly conventional ferroelectrics and HfO2-based ferroelectrics, and clarify how ferroelectrics can be used in neuromorphic applications such as synaptic weights and neurons
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