RRAM technology has made significant progress in the past decade as a competitive candidate for the next generation non-volatile memory (NVM). This lecture is a comprehensive tutorial of metal oxide-based RRAM technology from device fabrication to array architecture design. State-of-the-art RRAM device performances, characterization, and modeling techniques are summarized, and the design considerations of the RRAM integration to large-scale array with peripheral circuits are discussed. Chapter 2 introduces the RRAM device fabrication techniques and methods to eliminate the forming process, and will show its scalability down to sub-10 nm regime. Then the device performances such as programming speed, variability control, and multi-level operation are presented, and finally the reliability issues such as cycling endurance and data retention are discussed. Chapter 3 discusses the RRAM physical mechanism, and the materials characterization techniques to observe the conductive filaments and the electrical characterization techniques to study the electronic conduction processes. It also presents the numerical device modeling techniques for simulating the evolution of the conductive filaments as well as the compact device modeling techniques for circuit-level design. Chapter 4 discusses the two common RRAM array architectures for large-scale integration: one-transistor-one-resistor (1T1R) and cross-point architecture with selector. The write/read schemes are presented and the peripheral circuitry design considerations are discussed. Finally, a 3D integration approach is introduced for building ultra-high density RRAM array. Chapter 5 is a brief summary and will give an outlook for RRAM's potential novel applications beyond the NVM applications.

This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.

This book covers semiconductor memory technologies from device bit-cell structures to memory array design with an emphasis on recent industry scaling trends and cutting-edge technologies. The first part of the book discusses the mainstream semiconductor memory technologies. The second part of the book discusses the emerging memory candidates that may have the potential to change the memory hierarchy, and surveys new applications of memory technologies for machine/deep learning applications. This book is intended for graduate students in electrical and computer engineering programs and researchers or industry professionals in semiconductors and microelectronics. Explains the design of basic memory bit-cells including 6-transistor SRAM, 1-transistor-1-capacitor DRAM, and floating gate/charge trap FLASH transistor Examines the design of the peripheral circuits including the sense amplifier and array-level organization for the memory array Examines industry trends of memory technologies such as FinFET based SRAM, High-Bandwidth-Memory (HBM), 3D NAND Flash, and 3D X-point array Discusses the prospects and challenges of emerging memory technologies such as PCM, RRAM, STT-MRAM/SOT-MRAM and FeRAM/FeFET Explores the new applications such as in-memory computing for AI hardware acceleration.