Wide-Gap Perovskite via Synergetic Surface Passivation and Its Application toward Efficient Stacked Tandem Photovoltaics.

Superior bandgap tunability enables solution-processed halide perovskite a promising candidate for multi-junction photovoltaics (PVs). Particularly, optically coupling wide-gap perovskite by stacking with commercially available PVs such as silicon and CIGS (also known as 4-terminal tandem) simplifies the technology transfer process, and further advances the commercialization potential of perovskite technology. However, compared with matured PV materials and the phase-pure FAPbI3 , wide-gap perovskite still suffers from huge voltage deficits. Here, the authors take advantage of the synergetic effect behind a sequential fluoride and organic ammonium salt surface passivation strategy to control non-radiative energy losses, and obtained a 17.7% efficiency in infrared-transparent wide-gap perovskite solar cells (21.1% for opaque device), and achieved efficiencies of over 25% when stacked with commercial Si and CIGS products with original PCEs of 18-20% under a 4-terminal working condition.

Spike Encoding with Optic Sensory Neurons Enable a Pulse Coupled Neural Network for Ultraviolet Image Segmentation.

Drawing inspiration from biology, neuromorphic systems are of great interest in direct interaction and efficient processing of analogue signals in the real world and could be promising for the development of smart sensors. Here, we demonstrate an artificial sensory neuron consisting of an InGaZnO4 (IGZO4)-based optical sensor and NbOx-based oscillation neuron in series, which can simultaneously sense the optical information even beyond the visible light region and encode them into electrical impulses. Such artificial vision sensory neurons can convey visual information in a parallel manner analogous to biological vision systems, and the output spikes can be effectively processed by a pulse coupled neural network, demonstrating the capability of image segmentation out of a complex background. This study could facilitate the construction of artificial visual systems and pave the way for the development of light-driven neurorobotics, bioinspired optoelectronics, and neuromorphic computing.

Potassium-Presenting Zinc Oxide Surfaces Induce Vertical Phase Separation in Fullerene-Free Organic Photovoltaics.

Bulk heterojunction (BHJ) structure based organic photovoltaics (OPVs) have recently showed great potential for achieving high power conversion efficiencies (PCEs). An ideal BHJ structure would feature large donor/acceptor interfacial areas for efficient exciton dissociation and gradient distributions with high donor and acceptor concentrations near the anode and cathode, respectively, for efficient charge extraction. However, the random mixing of donors and acceptors in the BHJ often suffers the severe charge recombination in the interface, resulting in poor charge extraction. Herein, we propose a new approach-treating the surface of the zinc oxide (ZnO) as an electron transport layer with potassium hydroxide-to induce vertical phase separation of an active layer incorporating the nonfullerene acceptor IT-4F. Density functional theory calculations suggested that the binding energy difference between IT-4F and the PBDB-T-2Cl, to the potassium (K)-presenting ZnO interface, is twice as strong as that for IT-4F and PBDB-T-2Cl to the untreated ZnO surface, such that it would induce more IT-4F moving toward the K-presenting ZnO interface than the untreated ZnO interface thermodynamically. Benefiting from efficient charge extraction, the best PCEs increased to 12.8% from 11.8% for PBDB-T-2Cl:IT-4F-based devices, to 12.6% from 11.6% for PBDB-T-2Cl:Y1-4F-based devices, to 13.5% from 12.2% for PBDB-T-2Cl:Y6-based devices, and to 15.7% from 15.1% for PM6:Y6-based devices.

Flexible low-power source-gated transistors with solution-processed metal-oxide semiconductors.

Source-gated transistors (SGTs) with Schottky barriers have emerged as extraordinary candidates for constructing low-power electronics by virtue of device simplicity, high gain, and low operation voltages. In this work, we demonstrate flexible low-power SGTs with solution processed In2O3 channels and Al2O3 gate dielectrics on ultrathin polymer substrates, exhibiting light area density (0.56 mg cm-2), low subthreshold swing (102 mV dec-1), low operation voltage (<2 V), fast saturation behaviors (0.2 V), and low power consumption (46.3 µW cm-2). These achievements pave the way for employing the unconventional SGTs in wearable applications where low-power dissipation and high mechanical flexibility are essential.

A Review for Compact Model of Thin-Film Transistors (TFTs).

Thin-film transistors (TFTs) have grown into a huge industry due to their broad applications in display, radio-frequency identification tags (RFID), logical calculation, etc. In order to bridge the gap between the fabrication process and the circuit design, compact model plays an indispensable role in the development and application of TFTs. The purpose of this review is to provide a theoretical description of compact models of TFTs with different active layers, such as polysilicon, amorphous silicon, organic and In-Ga-Zn-O (IGZO) semiconductors. Special attention is paid to the surface-potential-based compact models of silicon-based TFTs. With the understanding of both the charge transport characteristics and the requirement of TFTs in organic and IGZO TFTs, we have proposed the surface-potential-based compact models and the parameter extraction techniques. The proposed models can provide accurate circuit-level performance prediction and RFID circuit design, and pass the Gummel symmetry test (GST). Finally; the outlook on the compact models of TFTs is briefly discussed.

A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors.

Physically transient electronic devices that can disappear on demand have great application prospects in the field of information security, implantable biomedical systems, and environment friendly electronics. On the other hand, the memristor-based artificial synapse is a promising candidate for new generation neuromorphic computing systems in artificial intelligence applications. Therefore, a physically transient synapse based on memristors is highly desirable for security neuromorphic computing and bio-integrated systems. Here, this is the first presentation of fully degradable biomimetic synaptic devices based on a W/MgO/ZnO/Mo memristor on a silk protein substrate, which show remarkable information storage and synaptic characteristics including long-term potentiation (LTP), long-term depression (LTD) and spike timing dependent plasticity (STDP) behaviors. Moreover, to emulate the apoptotic process of biological neurons, the transient synapse devices can be dissolved completely in phosphate-buffered saline solution (PBS) or deionized (DI) water in 7 min. This work opens the route to security neuromorphic computing for smart security and defense electronic systems, as well as for neuro-medicine and implantable electronic systems.

Full imitation of synaptic metaplasticity based on memristor devices.

Neuromorphic engineering is a promising technology for developing new computing systems owing to the low-power operation and the massive parallelism similarity to the human brain. Optimal function of neuronal networks requires interplay between rapid forms of Hebbian plasticity and homeostatic mechanisms that adjust the threshold for plasticity, termed metaplasticity. Metaplasticity has important implications in synapses and is barely addressed in neuromorphic devices. An understanding of metaplasticity might yield new insights into how the modification of synapses is regulated and how information is stored by synapses in the brain. Here, we propose a method to imitate the metaplasticity inhibition of long-term potentiation (MILTP) for the first time based on memristors. In addition, the metaplasticity facilitation of long-term potentiation (MFLTP) and the metaplasticity facilitation of long-term depression (MFLTD) are also achieved. Moreover, the mechanisms of metaplasticity in memristors are discussed. Additionally, the proposed method to mimic the metaplasticity is verified by three different memristor devices including oxide-based resistive memory (OxRAM), interface switching random access memory, and conductive bridging random access memory (CBRAM). This is a further step toward developing fully bio-realistic artificial synapses using memristors. The findings in this study will deepen our understanding of metaplasticity, as well as provide new insight into bio-realistic neuromorphic engineering.