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Strategic Fermi Level Engineering of Donor-Acceptor Self-Assembled Monolayer toward Ultrahigh Paired-Pulse
Ya-Shuan Wu1, Wei-Cheng Chen1, Yi-Sa Lin2
1Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan.
Abstract:
Given the rapid advancement of artificial intelligence and the demand for low-power computing, photosynaptic transistors have emerged as promising candidates by integrating sensing, memory, and processing to mimic biological synapses. However, simultaneously realizing high paired-pulse facilitation (PPF) and low energy consumption in an artificial synapse, as biological ones, remains challenging. This property should be achieved by balancing the high carrier density while maintaining a low conductivity in the photoresponsive/charge-trapping electret; however, these prerequisites are mutually contradictory. To achieve this goal, a series of electron acceptors, 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ), tris(pentafluorophenyl)borane (BCF), and their Lewis-paired F4BCF, are introduced to the orderly anchored and segregated pyrene self-assembled monolayers for Fermi level modulation of the charge-trapping electret. The high electron deficiency of F4BCF induces strong donor-acceptor interaction with pyrene, lowering its Fermi level and stabilizing charges through charge transfer. This molecular design strategy yields a record-high PPF ratio of 293% and an ultralow energy consumption of 2.96 × 10-19 J, supporting low-power and multilevel memory characteristics analogous to those of biological synapses. Finally, the demonstrated image preprocessing highlights its potential for neuromorphic visual computing. This study highlights the effectiveness of Lewis-paired acceptor engineering as a powerful molecular strategy for modulating the Fermi level. By combining the segregated anchored SAM electret, a decent balance between high carrier density and low conductivity is achieved, thereby realizing the highest PPF ratio and the lowest energy consumption simultaneously among the reported systems. This outperformance underscores its potential for next-generation low-power optoelectronic neuromorphic devices.

