New memristor wafer integration technology from DGIST paves the way for brain-like AI chips

□ A research team led by Professor Sanghyeon Choi from the Department of Electrical Engineering and Computer Science at DGIST (President Kunwoo Lee) successfully developed the “memristor,” which is gaining recognition as a next-generation semiconductor device, through mass-integration at the wafer scale. This study proposes a new technological platform for implementing a highly integrated AI semiconductor replicating the human brain, overcoming the limitations of conventional semiconductors.

□ The human brain contains about 100 billion neurons and around 100 trillion synapses, allowing it to store and process enormous amounts of information within a compact space. Next-generation AI research aims to develop “brain-like AI chips” that replicate this structure. Yet, current AI semiconductors remain far less efficient than the human brain, largely because of their intricate circuitry and substantial power requirements.

□ The memristor is an emerging alternative option that can overcome these limitations. As a semiconductor device capable of remembering the amount of current flowed, it simultaneously executes memory and computation tasks. Owing to its simple architecture, the circuit can be configured with a much higher density than typical semiconductors. Specifically, an arrangement in a crossbar format enables dozens of times more information to be stored in the same area, compared to SRAM.

□ However, memristor integration technology has so far been limited to small-scale experimental demonstrations. The main reasons include process complexity, low yield (product completion rate), voltage loss, and current leakage, all of which have hindered its expansion to large-scale wafer production.

□ Thus, Professor Choi and his team carried out joint research with Dr. Dmitri Strukov’s group at UC Santa Barbara and introduced a new approach of “co-designing material, component, circuit, and algorithm.” This method enabled the implementation of a memristor crossbar circuit that achieved an approximately 95% yield on a 4-inch wafer without requiring a complex fabrication process.

□ Furthermore, the research team successfully demonstrated a 3D vertical stacking structure. This signifies the possibility of a memristor-based circuit being expanded into a large-scale AI computation system in the future.

□ In addition, when a spiking neural network was applied based on the proposed technology, notable efficiency and stable execution were confirmed in actual AI computation.

□ Professor Choi stated, “This study proposed a method for improving memristor integration technology, which had been limited in the past” and added, “We are expecting it to lead to the development of a next-generation semiconductor platform in the future.”

□ This study was supported by the U.S. National Science Foundation, Industrial Innovation Talent Growth Support Program of the Korea Institute for Advancement of Technology, and Engineering Academic Research Support Program of the National Research Foundation of Korea’s Science. The research, led by Professor Choi of DGIST as both the first and corresponding author, with Professor Dmitri Strukov of UC Santa Barbara as a co-author, was published in October in the prestigious multidisciplinary journal Nature Communications.