Junctionless Poly-GeSn Ferroelectric Thin-Film Transistors with Improved Reliability by Interface Engineering for Neuromorphic Computing

Abstract

Ferroelectric HfZrO_<i>x</i> (Fe-HZO) with a larger remnant polarization (<i>P</i>_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 <i>P</i>_r degradation up to 10⁶ cycles (±2.7 MV/1 ms) and 96% <i>P</i>_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 (<i>G</i>) states of 80 levels (>6 bit), small linearity for potentiation and depression of -0.83 and 0.62, high symmetry, and moderate <i>G</i>_max/<i>G</i>_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.