The rapid expansion of data has made global access easier, but it also demands increasing amounts of energy for data storage and processing. In response, neuromorphic electronics, inspired by the functionality of biological neurons and synapses, have emerged as a growing area of research. These devices enable in-memory computing, helping to overcome the "von Neumann bottleneck", a limitation caused by the separation of memory and processing units in traditional von Neumann architecture. By leveraging multi-bit non-volatility, biologically inspired features, and Ohm's law, synaptic devices show great potential for reducing energy consumption in multiplication and accumulation operations. Within the various non-volatile memory technologies available, flash memory stands out as a highly competitive option for storing large volumes of data. This review highlights recent advancements in neuromorphic computing that utilize NOR, AND, and NAND flash memory. This review also delves into the array architecture, operational methods, and electrical properties of NOR, AND, and NAND flash memory, emphasizing its application in different neural network designs. By providing a detailed overview of flash memory-based neuromorphic computing, this review offers valuable insights into optimizing its use across diverse applications.
