Unveiling the Role of Side Chain for Improving Nonvolatile Characteristics of Conjugated Polymers‐Based Artificial Synapse

Abstract

Interest has grown in services that consume a significant amount of energy, such as large language models (LLMs), and research is being conducted worldwide on synaptic devices for neuromorphic hardware. However, various complex processes are problematic for the implementation of synaptic properties. Here, synaptic characteristics are implemented through a novel method, namely side chain control of conjugated polymers. The developed devices exhibit the characteristics of the biological brain, especially spike‐timing‐dependent plasticity (STDP), high‐pass filtering, and long‐term potentiation/depression (LTP/D). Moreover, the fabricated synaptic devices show enhanced nonvolatile characteristics, such as long retention time (≈102 s), high ratio of Gmax/Gmin, high linearity, and reliable cyclic endurance (≈103 pulses). This study presents a new pathway for next‐generation neuromorphic computing by modulating conjugated polymers with side chain control, thereby achieving high‐performance synaptic properties.

Enhanced Anion Interaction by Polarity Control on CNTVT:SVS Copolymers for Improving Nonvolatile Characteristics in Neuromorphic Computing

“…Measurement and Analysis. 1 H NMR and 13 C NMR spectra were recorded with Model Avance 300 and Model DRX 500 MHz FT-NMR Bruker spectrometers, and chemical shifts (ppm) were reported with tetramethylsilane (TMS) as an internal standard (Figure S1). Thermogravimetric analysis (TGA) was performed on a TA 2050 thermogravimetric analyzer under a nitrogen atmosphere at a rate of 10 °C/min.…”

“…Various synaptic devices, such as memristors and memtransistors, have been reported to mimic the human brain for processing large amounts of data. − Memristors are essentially two-terminal devices that resemble biological synapses. − However, the absence of gate tunability makes it difficult to achieve precise neuromorphic learning, and it also allows sneak paths for current to flow in unwanted directions. , In contrast, memtransistors, as three-terminal devices, offer a distinct advantage over memristors by enabling the precise emulation of synaptic functions through gate tunability. This capability, which cannot be achieved using memristors, makes memtransistors highly promising candidates for the development of artificial synapses. , In particular, electrolyte gate transistors (EGTs), which use an ionic electrolyte as the gate layer, utilize the high capacitance of the electrolyte based on its high ionic conductivity to enable synaptic functions at low operating voltages. − …”

Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors

“…In recent years, synaptic transistors, both inorganic and organic, realizing nonlinear responses suitable for use as NAFs in NNs, have been actively researched. − Several groups have mimicked the behavior of human synaptic signals through gate voltage stimulation. Lee’s group improved the nonvolatility of artificial synapses by modifying the side chains of conjugated polymers . Some of us explored ambipolar conjugated polymer transistors to study artificial synaptic behavior .…”

“…Lee’s group improved the nonvolatility of artificial synapses by modifying the side chains of conjugated polymers. 16 Some of us explored ambipolar conjugated polymer transistors to study artificial synaptic behavior. 22 In addition, we studied the active channel with ionic dopants to mimic synaptic behavior in artificial synapses.…”