Symmetric Ambipolar Thin-Film Transistors and High-Gain CMOS-like Inverters Using Environmentally Friendly Copper Nitride

Oxide semiconductor thin-film transistors (TFTs) are currently used as the fundamental building blocks in commercial flat-panel displays because of the excellent performance of n-channel TFTs. However, except for a few materials, their p-channel performances have not been acceptable. Although some p-type oxide semiconductors exhibit superior hole transport properties, their TFT performances are greatly deteriorated, which is a major obstacle in the development of complementary metal-oxide-semiconductor (CMOS) circuits. Herein, an ionic nitride semiconductor, copper nitride (Cu₃N), composed of environmentally benign elements is shown to exhibit highly symmetric hole and electron transport, indicating its suitability for application in CMOS circuits. We performed a two-step investigation. The first step was to examine the ultimate potential of Cu₃N using an electric-double-layer transistor structure with epitaxial Cu₃N channels measured at 220 K, which exhibited ambipolar operation with hole and electron mobilities of ∼5 and ∼10 cm² V^-1 s^-1, respectively, and a high on/off ratio of ∼10⁵. The second step is to demonstrate the feasibility of TFT circuits with a polycrystalline channel on non-single-crystal (SiO₂/Si) substrates. CMOS-like inverters composed of two polycrystalline Cu₃N ambipolar TFTs on a SiO₂/Si substrate exhibited a high voltage gain of ∼100.

Ionotronic Halide Perovskite Drift-Diffusive Synapses for Low-Power Neuromorphic Computation

Emulation of brain-like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift-diffusive ionic kinetics would enable energy-efficient analog-like switching of metastable conductance states. Here, ionic-electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short- and long-term plasticity rules like paired-pulse facilitation and spike-time-dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting, and fault tolerance analogous to the human brain. Network-level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy-efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material.