Core Insights - Storage technology is essential for modern computing systems, evolving from basic data storage to advanced applications like in-memory computing, which enhances efficiency by reducing data transfer between processors and memory [1][3] - Emerging non-volatile memory (eNVM) technologies, such as ReRAM, MRAM, FeRAM, and PCM, are promising alternatives to traditional volatile memory, maintaining data integrity even when power is lost [3][4] - The transition from traditional digital computing to brain-inspired computing is driven by the need for more efficient architectures that can handle the demands of AI and ML applications [25][28] Group 1: Emerging Storage Technologies - eNVMs are capable of retaining data without power, unlike traditional RAM, and include various architectures that are being explored for their potential in AI and ML [3][4] - The development of new materials and device architectures is crucial for advancing eNVMs, with a focus on overcoming challenges related to performance and scalability [3][10] - The integration of two-dimensional materials in storage devices is expected to revolutionize the field, offering high density and low power consumption [11][21] Group 2: Non-Volatile Memory in Post-CMOS Era - Non-volatile memory is seen as a key player in the post-CMOS microelectronics era, addressing the limitations of the von Neumann architecture and enabling new computing paradigms [5][8] - The current landscape of non-volatile memory research dates back to the 1960s, with significant advancements made in recent years, particularly in flash memory technology [5][8] - The future of non-volatile memory includes a focus on flexible and wearable electronics, driven by the demand for devices that can withstand mechanical stress while retaining data [15][16] Group 3: Challenges and Opportunities - The transition to brain-inspired computing architectures presents both opportunities and challenges, particularly in terms of energy efficiency and system performance [25][28] - Key challenges include material synthesis, manufacturing precision, and the integration of new storage technologies with existing CMOS processes [19][20][22] - Addressing these challenges is essential for the advancement of storage technologies, which are critical for the future of computing, AI, and advanced sensing applications [29][30]