Synaptic transistors for neuromorphic computing: unveiling progress and addressing challenges

Abstract Synaptic transistors (STs) are gaining valuable interest as they hold a significant potential for neuromorphic hardware implementation. They are key elements in emulating biological synaptic behavior as they have the ability to perform memory storage and parallel information processing. Their applications extend beyond AI, biomedicine, and wearable electronics. For these devices to efficiently mimic biological counterparts’ behavior, further understanding of their operating modes and fabrication methods are necessary. Energy consumption, speed, and electrical and mechanical resistance are the main performance indicators. The fabrication of efficient synaptic devices is, in fact, associated to a new generation of technologies with abilities very close to the human brain performance. This review is specifically a critical examination of the fundamental concepts of STs. It summarizes the latest research advances concerning five major categories: floating-gate, electrolyte-gated, ferroelectric-gated, optoelectronic, and electret-based STs. For every transistor type, a description of device performance in terms of structure design and materials is highlighted along with a comparison of their advantages, limitations and practical potential. The key performance metrics associated to operating voltage, speed, endurance, retention, energy consumption, conductance linearity, dynamic range, scalability, CMOS compatibility, and others are identified and analyzed. The criteria for their fabrication with an emphasis on similarities with the human brain operation are emphasized. Future challenges and integration prospects are also highlighted according to the new wave of neuromorphic applications. The review demonstrates that, despite the significant progress, implementation of STs remains in its early stage development. Research effort must be aligned with the future requirements of data-dominant and AI driven applications in terms of high density integration, storage and big data management.