J. Alexander Liddle

Vice President, Research and Development, Canon Nanotechnologies

Inkjet Nanoimprint Lithography: Technology and Applications

Artificial intelligence (AI) has become ubiquitous in daily life; however, most recent breakthroughs have been driven primarily by advances in algorithms and software, while progress in underlying hardware has lagged behind. To sustain continued growth in computing performance and energy efficiency, intensive efforts are underway to move the hardware beyond Moore’s Law by exploring new materials, devices, technologies, and computing paradigms. In particular, emerging devices leveraging physical mechanisms such as spin, phase transitions, and ionic charge transport, together with computing paradigms like in-memory computing, are attracting significant attention as promising platforms for next-generation, high-efficiency AI hardware.

This short course highlights the critical role of materials and integration in enabling emerging memory-centric AI hardware, bridging device-level physics with system-level requirements for analog in-memory computing in artificial neural networks. The course begins with an overview of key memory technologies actively pursued for AI computing, including SRAM, FLASH, phase-change memory, magnetoresistive random-access memory (MRAM), resistive random-access memory (RRAM), and ferroelectric memory. Building on this foundation, the course focuses on non-volatile memristive devices, which encode analog information through distinct resistance states enabled by ionic motion and defect dynamics. When organized into crossbar arrays, these devices naturally support massively parallel multiply–accumulate operations, leveraging physical laws to deliver high computational throughput and energy efficiency.

The course then addresses key challenges and practical solutions for integrating memristive devices into large-scale arrays, outlining a clear path from early, experimental fabrication to reliable, high-volume manufacturing of AI hardware accelerators. In particular, we will present a memristive system-on-a-chip (SoC) that integrates memristive crossbar arrays with CMOS technology on a single chip, highlighting our recent lab-to-fab transfer initiative supported by the CHIPS Act. Finally, the course will showcase the use of memristive SoCs across a range of edge-intelligence applications, including neural networks and signal processing.

About J. Alexander Liddle

J. Alexander Liddle received his undergraduate and doctoral degrees in materials science from the University of Oxford. From there, he moved to Bell Laboratories, where he worked on developing technology for advanced semiconductor lithography and microelectromechanical systems for telecom applications. He then joined the Center for X-ray Optics at Lawrence Berkeley National Laboratory and was later involved in the startup of the Molecular Foundry nanoscience user facility at LBNL. Before joining Canon Nanotechnologies, he spent two decades at NIST, most recently as chief of the Microsystems and Nanotechnology Division. His research has focused on lithography and directed self-assembly for the creation and manufacture of advanced devices. He has over 275 published papers, serves on numerous advisory committees, and is a fellow of the American Physical Society.