Fast aging-aware timing analysis framework with temporal-spatial graph neural network

With the downscaling of CMOS technology, device aging induced by hot carrier injection and bias temperature instability effects poses severe challenges to timing analysis of digital circuits. In this work, a fast aging-aware timing analysis framework based on temporal-spatial graph neural network is proposed for the first time. The temporal-spatial graph neural network takes gated tanh unit (GTU) as the temporal network to extract devices’ degradation from dynamic biases, and takes inductive GraphSAGE as the spatial network to obtain whole graph information from circuit topology and output circuit aging delay. With comprehensive comparison among the network candidates, the combination of gated tanh unit (GTU) and GraphSAGE presents the highest accuracy in predicting the standard cell aging delay. Owing to the superior features capture capability, this framework significantly improves the aging prediction efficiency under various operation conditions, especially facing the iterations of usage scenario, design version and process design kit. Compared with the conventional flow, the average acceleration ratio of our temporal-spatial network in predicting aging delay is more than 200 times. Furthermore, this framework is demonstrated with ADDER and FIFO circuits in timing analysis at the end of life. Thus, this work is helpful to the aging-aware circuit design in nano-scale technology.</p

Diversity-Oriented Enzymatic Modular Assembly of ABO Histo-blood Group Antigens

Enzymatic synthesis of all 15 naturally occurring human ABH antigens was achieved using a diversity-oriented enzymatic modular assembly (EMA) strategy. Three enzyme modules were developed, each one-pot multienzyme module comprises a glycosyltransferase and one or two corresponding sugar nucleotide generating enzyme(s). These multienzyme cascade processes provide an efficient and convenient platform for collective synthesis of all 15 ABH antigens in three operationally simple steps from five readily available disaccharide acceptors and three simple free sugars as donor precursors

Successfully Engineering a Bacterial Sialyltransferase for Regioselective α2,6-sialylation

A β-galactoside α2,6-sialyltransferase from <i>Photobacterium damselae</i> (Pd2,6ST) that is capable of sialylating both terminal and internal galactose and <i>N</i>-acetylgalactosamine was herein redesigned for regioselectively producing terminal α2,6-sialosides. Guided by a recently developed bump-hole strategy, a series of mutations at Ala200 and Ser232 sites were created for reshaping the acceptor binding pocket. Finally, a Pd2,6ST double mutant A200Y/S232Y with an altered L-shaped acceptor binding pocket was identified to be a superior α2,6-sialyltransferase which can efficiently catalyze the regioselective α2,6-sialylation of galactose or <i>N</i>-acetylgalactosamine at the nonreducing end of a series of glycans. Meanwhile, A200Y/S232Y remains flexible donor substrate specificity and is able to transfer Neu5Ac, Neu5Gc, and KDN