How SK Hynix Builds Artificial Synapses for Brain-Like Computer Chips
A design for a tiny hardware component that mimics biological brain connections to help computers learn and process information like a human brain.
Original patent title: “Synapse and neuromorphic device including the same”
A design for a tiny hardware component that mimics biological brain connections to help computers learn and process information like a human brain. Granted to SK Hynix Inc in 2020 with 19 claims.
Key facts
Coverage
What does this patent actually cover?
This patent describes a physical component called a synapse designed for neuromorphic computing, which is a way of building computer chips that mimic the structure of the human brain. The core mechanism involves a 'reactive metal layer' shaped like a tapering wedge or staircase, positioned between two electrodes and an oxygen-containing layer. When voltage is applied, oxygen ions move to react with the metal, creating or removing a thin layer of insulating oxide. This change in the oxide layer alters the electrical conductivity of the device, effectively storing memory or 'weight' in the system, similar to how biological synapses strengthen or weaken their connections based on activity.
The gap
What does this patent NOT cover?
- Does not cover software-based neural networks that run on traditional CPUs or GPUs.
- Does not cover synapses that rely on biological materials or organic chemistry.
- Does not cover devices where the reactive metal layer has a uniform, non-tapered width.
- Does not cover memory cells that do not use oxygen ion migration to change conductivity.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
By tapering the width of the reactive metal layer, the device gains precise control over the area where the insulating oxide forms, which allows for more stable and predictable changes in electrical resistance.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Experimental neuromorphic processor prototypes
Hardware-accelerated AI inference chips
Next-generation non-volatile memory research
Why it matters
The bigger picture
Traditional computers are inefficient at tasks like pattern recognition because they separate memory from processing. Neuromorphic devices like this one aim to integrate memory directly into the processing unit, potentially allowing AI to run on tiny amounts of power. This is a critical step for moving advanced machine learning out of massive data centers and into local devices like phones or sensors.
Filed
December 22, 2016
Granted
February 18, 2020
Market context
Who's building on this
Companies in this space
SK Hynix is a major player in memory hardware and is actively researching how to integrate these synaptic structures into commercial chip architectures. Other large semiconductor firms like Intel, IBM, and various startups in the neuromorphic space are also pursuing similar resistive-switching memory technologies to enable brain-inspired computing.
Market impact
This patent represents a shift toward hardware-level AI optimization, moving away from general-purpose computing toward specialized 'brain-like' hardware. While not yet a standard, this type of technology is essential for the future of edge AI, where devices must perform complex learning tasks without connecting to the cloud.
Claim 1 — Plain English
What this patent covers
This patent describes a physical component called a synapse designed for neuromorphic computing, which is a way of building computer chips that mimic the structure of the human brain. The core mechanism involves a 'reactive metal layer' shaped like a tapering wedge or staircase, positioned between two electrodes and an oxygen-containing layer. When voltage is applied, oxygen ions move to react with the metal, creating or removing a thin layer of insulating oxide. This change in the oxide layer alters the electrical conductivity of the device, effectively storing memory or 'weight' in the system, similar to how biological synapses strengthen or weaken their connections based on activity.
The clever bit
By tapering the width of the reactive metal layer, the device gains precise control over the area where the insulating oxide forms, which allows for more stable and predictable changes in electrical resistance.
What it does not cover
- Does not cover software-based neural networks that run on traditional CPUs or GPUs.
- Does not cover synapses that rely on biological materials or organic chemistry.
- Does not cover devices where the reactive metal layer has a uniform, non-tapered width.
- Does not cover memory cells that do not use oxygen ion migration to change conductivity.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
13/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
10/20
Granted 5–10 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$31K – $100K
Midpoint $62K · 10.5 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
19 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
PARK, S., & Lee, H. (2020). How SK Hynix Builds Artificial Synapses for Brain-Like Computer Chips (U.S. Patent No. 10,565,497). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/10565497/multi-turn-conversational-ai
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How SK Hynix Builds Artificial Synapses for Brain-Like Computer Chips cover?
A design for a tiny hardware component that mimics biological brain connections to help computers learn and process information like a human brain.
Who owns patent US 10565497?
SK Hynix Inc owns this patent, granted in 2020.
When does this patent expire?
This patent is expected to expire on February 18, 2040, when the invention enters the public domain.
What problem does this patent solve?
Traditional computers are inefficient at tasks like pattern recognition because they separate memory from processing. Neuromorphic devices like this one aim to integrate memory directly into the processing unit, potentially allowing AI to run on tiny amounts of power. This is a critical step for moving advanced machine learning out of massive data centers and into local devices like phones or sensors.
What does this patent NOT cover?
Does not cover software-based neural networks that run on traditional CPUs or GPUs.
Patent monitoring