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In this article, a novel machine learning regression-based single-event transient (SET) modeling method is proposed. The proposed method can obtain a reasonable and accurate SET model without introducing complex physical mechanisms, which are not suitable for circuit-level simulation, into the model. To capture the essential physics behind these current transients caused by SET in the circuit-level simulations, we collect plenty of SET current data under different conditions [e.g., different linear energy transfer (LET), different drain bias voltage, different strike position, etc.] to train the SET model. To show the effectiveness of the proposed modeling method, we build a SET pulse current model by learning SET current data of Semiconductor Manufacturing International Corporation (SMIC) 130-nm bulk CMOS obtained by TCAD simulation. A multilayer feedforward neural network is used to build the SET pulse current model and the built SET model takes into account the dependence of time, LETs, drain bias voltages, and strike positions. The results from the model are validated with the simulation from TCAD. The trained SET pulse current model is implemented as a Verilog-A current source in the Cadence Specter circuit simulator, and an inverter with five fan-outs is used to show the practicability and reasonableness of the proposed SET pulse current model for circuit-level single-event effect (SEE) simulation.

The copolymer P(AM-DMC), one kind of cationic polyacrylamide, was synthesized by using acrylamide (AM) and methacryloxyethyltrimethyl ammonium chloride (DMC) as monomers with ultraviolet initiation. The copolymer was characterized by Fourier transform infrared spectroscopy. The composition of the copolymer was determined from the chlorine content by silver nitrate titration. The effects of monomer feed ratio and conversion on the composition of copolymer were discussed. Reactivity ratios were determined by the Fineman-Ross, Kelen-Tudos and Yezrielev-Brokhina-Roskin methods, indicating that their positive order calculation results were very different with those of reverse order, when the reactivity ratios were determined by Fineman-Ross. However, their positive order and reverse order calculation results were similar when the reactivity ratios were determined by the Kelen-Tudos and Yezrielev-Brokhina-Roskin methods. Therefore, the reactivity ratios were determined using the average results of the Kelen-Tudos and Yezrielev-Brokhina-Roskin methods. The reactivity ratios of AM and DMC were 2.3398 and 0.2285, respectively. This result showed that the copolymerization of AM and DMC was a non-ideal random copolymerization. Furthermore, the content of AM segments decreased with the increase of AM feed content, but the content of DMC segments increased. Moreover, the sequence length distribution was irregular and narrow, because AM was prone to homopolymerization.

A new single step, efficient, and scalable process was developed for synthesis of olivary carbon microparticles with a diameter of 4–6 μm at the middle and a length 8–12 μm by pyrolysis (under 600 °C) of ethanol with manganese acetate as the promoter. The physical and chemical structures of the olivary carbon were investigated by X-ray powder diffraction, scanning and transmission electron microscopy and Fourier transform infrared spectroscopy. The content of the olivary carbon in the product is related to the pyrolysis temperature, the reaction time, and the dosage of the promoter. The content of the olivary carbon was over 90% of the product under the optimum condition. A possible formation process of the olivary carbon has been put forward according to the experimental data available.