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Improved ReRAM: A New Method for Memory Chip Manufacture
Case ID:
014-18-09
Web Published:
12/9/2025
A new method of producing ReRAM chips more suitable for neuromorphic computer systems, AI, and other applications. Background: Chip manufacturers are actively pursuing the development of powerful, next-generation memory chips. One of these emerging technologies is ReRAM (resistive random access memory) which can preserve massive amounts of RAM data on a single chip even in the event of complete power loss. A challenge to the technology is the need for data-storing filaments to form precisely in the thin layers of dielectric oxide that are essential to ReRAM chip functionality. Technology Overview: A new method of producing ReRAM chips developed at SUNY Polytechnic Institute addresses this problem, making ReRAM chips more suitable for neuromorphic computer systems, AI, and other applications.
Key features:
Dense non-volatile memory chip with ultra-fast processing times.
Unparalleled read/write reliability.
Analog-like output that compliments neuromorphic computer systems.
How it works:
Memory cell consists of a metal oxide switching layer sandwiched between two metal electrodes.
An electrical charge from the electrodes creates, via oxidation, data-storing filaments in the switching layer.
Specially coated electrode contacts and overhanging design of switching layer. allow for greater control of oxidation and properly formed filaments.
Advantages:
Standard BEOL processing flow means faster chip production time.
Fewer manufacturing steps for increased profitability.
Reduced power consumption for end-users.
Intellectual Property Summary: Patent application submitted,
WO2020131179A2
Licensing Potential: Development partner, Commercial partner Licensing Status: This technology is available for licensing. https://suny.technologypublisher.com/files/sites/014-18-09istock-178487234.jpg
Patent Information:
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Direct Link:
https://canberra-ip.technologypublisher.com/tech?title=Improved_ReRAM%3a_A_Ne w_Method_for_Memory_Chip_Manufacture
Keywords:
artificial intelligence
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For Information, Contact:
Karl-Heinz Schofalvi
The Research Foundation for The State University of New York
Karl-Heinz.Schofalvi@rfsuny.org