Ferroelectric HfZrO
x
(Fe-HZO)
with a larger remnant polarization (P
r) is achieved by using a poly-GeSn film as a channel material as
compared with a poly-Ge film because of the lower thermal expansion
that induces higher stress. Then two-stage interface engineering of
junctionless poly-GeSn (Sn of ∼5.1%) ferroelectric thin-film
transistors (Fe-TFTs) based on HZO was employed to improve the reliability
characteristics. With stage I of NH3 plasma treatment on
poly-GeSn and subsequent stage II of Ta2O5 interfacial
layer growth, the interfacial quality between Fe-HZO and the poly-GeSn
channel is greatly improved, which in turn enhances the reliability
performance in terms of negligible P
r degradation
up to 106 cycles (±2.7 MV/1 ms) and 96% P
r after a 10 year retention at 85 °C. Furthermore,
to emulate the synapse plasticity of the human brain for neuromorphic
computing, besides manifesting the capability of short-term plasticity,
the devices also exhibit long-term plasticity with the characteristics
of analog conductance (G) states of 80 levels (>6
bit), small linearity for potentiation and depression of −0.83
and 0.62, high symmetry, and moderate G
max/G
min of 9.6. By employing deep neural
network, the neuromorphic system with poly-GeSn Fe-TFT synaptic devices
achieves 91.4% pattern recognition accuracy. In addition, the learning
algorithm of spike-timing-dependent plasticity based on spiking neural
network is demonstrated as well. The results are promising for on-chip
training, making it possible to implement neuromorphic computing by
monolithic 3D ICs based on poly-GeSn Fe-TFTs.