Recognizing Spatiotemporal Features by a Neuromorphic Network with Highly Reliable Ferroelectric Capacitors on Epitaxial GeSn Film.

Epitaxial GeSn (epi-GeSn) shows the capability to form ferroelectric capacitors (FeCAPs) with a higher remanent polarization (Pr) than epi-Ge. With the interface engineering by a high-k AlON, the reliability of the epi-GeSn-based FeCAPs is enhanced. Using the highly reliable FeCAP in series with a resistor as the synapse and axon, a simplified neuromorphic network based on a differentiator circuit is proposed. The network not only holds the leaky integrate-and-fire (LIF) function but is also capable of recognizing the spatiotemporal features, which sets it apart from other LIF neurons arising from the FeCAP-modulated leaky behavior of the potential on the axon by spiking-time-dependent plasticity. Furthermore, it is more energy efficient to operate, nondestructive to read, and simpler to fabricate by employing FeCAPs, making it eligible for emergent spiking neural network hardware accelerators.

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 (Pr) 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 Pr degradation up to 106 cycles (±2.7 MV/1 ms) and 96% Pr 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 Gmax/Gmin 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.