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

Ferroelectric HfZrOx (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 similar to 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 10(6) cycles (+/- 2.7 MV/1 ms) and 96% P-r after a 10 year retention at 85 degrees 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.