R&D: All-silicon Non-volatile Optical Memory based on Photon Avalanche-induced Trapping
Authors demonstrate non-volatile optical memory exclusively using most common semiconductor material, silicon.
This is a Press Release edited by StorageNewsletter.com on February 6, 2025 at 2:00 pmCommunications Physics has published an article written by Yuan Yuan, Hewlett Packard Labs, Hewlett Packard Enterprise, Milpitas, CA, 95035, USA, and Department of Electrical and Computer Engineering, Northeastern University, Oakland, CA, 94613, USA, Yiwei Peng, Hewlett Packard Labs, Hewlett Packard Enterprise, Milpitas, CA, 95035, USA, Stanley Cheung, Hewlett Packard Labs, Hewlett Packard Enterprise, Milpitas, CA, 95035, USA, and Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27695, USA, Wayne V. Sorin, Sean Hooten, Zhihong Huang, Hewlett Packard Labs, Hewlett Packard Enterprise, Milpitas, CA, 95035, USA, Di Liang, Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI, 48109, USA, Jiuyi Zhang, Marco Fiorentino, and Raymond G. Beausoleil, Hewlett Packard Labs, Hewlett Packard Enterprise, Milpitas, CA, 95035, USA.
Abstract: “Implementing on-chip non-volatile optical memories has long been an actively pursued goal, promising significant enhancements in the capability and energy efficiency of photonic integrated circuits. Here, we demonstrate an non-volatile optical memory exclusively using the most common semiconductor material, silicon. By manipulating the photon avalanche effect, we introduce a trapping effect at the silicon-silicon oxide interface, which in turn demonstrates a non-volatile reprogrammable optical memory cell with a record-high 4-bit encoding, robust retention and endurance. This silicon avalanche-induced trapping memory provides a distinctively cost-efficient and high-reliability route to realize optical data storage in standard silicon foundry processes. We demonstrate its applications in trimming in optical interconnects and in-memory computing. Our in-memory computing test case reduces energy consumption by approximately 83% compared to conventional optical approaches.“