Nam San, Kang Donghyun, Jeon Seong-Pil, Nam Dayul, Jo Jeong-Wan, Park Sang-Joon, Lee Jiyong, Kim Myung-Gil, Ha Tae-Jun, Park Sung Kyu, Kim Yong-Hoon
School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
Small. 2025 Feb;21(7):e2409510. doi: 10.1002/smll.202409510. Epub 2025 Jan 5.
Homeostasis is essential in biological neural networks, optimizing information processing and experience-dependent learning by maintaining the balance of neuronal activity. However, conventional two-terminal memristors have limitations in implementing homeostatic functions due to the absence of global regulation ability. Here, three-terminal oxide memtransistor-based homeostatic synapses are demonstrated to perform highly linear synaptic weight update and enhanced accuracy in neuromorphic computing. Particularly, by leveraging the gate control of contact-engineered indium-gallium-zinc-oxide (IGZO) memtransistor, synaptic weight scaling is enabled for high-linearity and precision neuromorphic computing. Moreover, sinusoidal control of gate voltage is demonstrated, possibly enabling the emulation of higher-order synaptic functions. The device structure of IGZO memtransistor is optimized regarding the source/drain electrode materials and an interfacial layer inserted between the IGZO channel and source electrode. As a result, memtransistors exhibiting high current switching ratio of >10 and reliable endurance characteristics are obtained. Furthermore, through the adaptation of synaptic scaling, emulating the homeostasis, non-linearity values of 0.01 and -0.01 are achieved for potentiation and depression, respectively, exhibiting a recognition accuracy of 91.77% for digit images. It is envisioned that the contact-engineered IGZO memtransistors hold significant promise for implementing the homeostasis in neuromorphic computing for high linearity and high efficiency.
内稳态在生物神经网络中至关重要,它通过维持神经元活动的平衡来优化信息处理和依赖经验的学习。然而,传统的两端忆阻器由于缺乏全局调节能力,在实现内稳态功能方面存在局限性。在此,基于三端氧化物忆阻晶体管的内稳态突触被证明在神经形态计算中能执行高度线性的突触权重更新并提高准确性。特别地,通过利用接触工程化铟镓锌氧化物(IGZO)忆阻晶体管的栅极控制,实现了突触权重缩放,以用于高线性和高精度神经形态计算。此外,还展示了栅极电压的正弦控制,这可能使高阶突触功能的模拟成为可能。IGZO忆阻晶体管的器件结构在源极/漏极电极材料以及插入IGZO沟道和源极之间的界面层方面进行了优化。结果,获得了具有大于10的高电流开关比和可靠耐久性的忆阻晶体管。此外,通过采用突触缩放来模拟内稳态,在增强和抑制时分别实现了0.01和 -0.01的非线性值,对于数字图像展现出91.77%的识别准确率。可以预见,接触工程化的IGZO忆阻晶体管在实现神经形态计算中的内稳态以实现高线性和高效率方面具有巨大潜力。