Jae Gwang Lim ^1,2 Sang Min Lee ^1,2 Sung-Jae Park ^1,2 Joon Young Kwak ³ YeonJoo Jeong ¹ Jaewook Kim ¹ Suyoun Lee ¹ Jongkil Park ¹ Gyu Weon Hwang ¹ Kyeong-Seok Lee ¹ SEONGSIK PARK ¹ Byeong-Kwon Ju ¹ Hyun Jae Jang ¹ Jong-Keuk Park ¹ Inho Kim ^1*

• ¹ Korea Institute of Science and Technology (KIST), Bongdong-eup, Republic of Korea
• ² Korea University, Seoul, Republic of Korea
• ³ Ewha Womans University, Seoul, Seoul, Republic of Korea

In view of the growing volume of data, there is a notable research focus on hardware that offers high computational performance with low power consumption. Notably, neuromorphic computing, particularly when utilizing CMOS-based hardware, has demonstrated promising research outcomes. Furthermore, there is an increasing emphasis on the utilization of emerging synapse devices, such as non-volatile memory (NVM), with the objective of achieving enhanced energy and area efficiency. In this context, we designed a hardware system that employs memristors, a type of emerging synapse, for a 1T1R synapse. The operational characteristics of a memristor are dependent upon its configuration with the transistor, specifically whether it is located at the source (MOS) or the drain (MOD) of the transistor. Despite its importance, the determination of the 1T1R configuration based on the operating voltage of the memristor remains insufficiently explored in existing studies. To enable seamless array expansion, it is crucial to ensure that the unit cells are properly designed to operate reliably from the initial stages. Therefore, this relationship was investigated in detail, and corresponding design rules were proposed. SPICE model based on fabricated memristors and transistors was utilized. Using this model, the optimal transistor selection was determined and subsequently validated through simulation. To demonstrate the learning capabilities of neuromorphic computing, an SNN inference accelerator was implemented. This implementation utilized a 1T1R array constructed based on the validated 1T1R model developed during the process. The accuracy was evaluated using a reduced MNIST dataset. The results verified that the neural network operations inspired by brain functionality were successfully implemented in hardware with high precision and no errors. Additionally, traditional ADC and DAC, commonly used in DNN research, were replaced with DPI and LIF neurons, resulting in a more compact design. The design was further stabilized by leveraging the low-pass filter effect of the DPI circuit, which effectively mitigated noise.