Find related topics. | Discover entities. | See connections. | Build a topical map.
Building topical map: Starting…
Single-event upset: History, Applications, Art & Measurement
A single-event upset (SEU), also known as a single-event error (SEE), is a change of state caused by one single ionizing particle (e.g. ions, electrons, photons) striking a sensitive node in a live micro-electronic device, such as in a microprocessor, semiconductor memory, or power transistors. The state change is a result of the free charge created by…
Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.
More settings
Single-event upset topic overview
The analysis highlights History, Applications, Art and Measurement as prominent areas in the source structure around Single-event upset.
Related topics
42
Source areas
6
Connected nodes
48
Extracted relationships
105
Concept neighborhoods
21
Bridge connections
48
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
SEUs and circuit design · 10 topics
Cause · 8 topics
History · 8 topics
Overview · 7 topics
Notable SEUs · 5 topics
Testing for SEU sensitivity · 4 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Single-event upset connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Single-event upset shows recurring relationship patterns in the source. For example, Single-event upset → Alpha Particles, Aug, AvionicsA Memory Soft Error, Boeing Radiation Effects Laboratory, Brigham Young University, California, Computer Memories, Corp, Cosmic Rays, Development, ED-26, Effect, Estimating Rates, Evaluation, FPGA, FPGAs, Goddard Space Flight Center, High-Performance SEU Hardened Flip-Flop, Highly Reliable SEU Hardened, IBM Journal Another extracted example is Single-event upset → Al Holman, Bevatron, Dan Binder, Dr, Edward, Further, Hughes, IBM, IEEE Transactions, In, Inch Cyclotron, James Ziegler, Lanford, Lawrence Berkeley National Laboratory's, May, Nuclear Science, Scientists Dr, SEU, Single-event, Smith. Use these groups to spot repeated connection types before inspecting the individual relationships.
Single-event upset
Top relations
related to Further reading· 69
Single-event upset → Alpha Particles, Aug, AvionicsA Memory Soft Error, Boeing Radiation Effects Laboratory, Brigham Young University, California, Computer Memories, Corp, Cosmic Rays, Development, ED-26, Effect, Estimating Rates, Evaluation, FPGA, FPGAs, Goddard Space Flight Center, High-Performance SEU Hardened Flip-Flop, Highly Reliable SEU Hardened, IBM Journal
related to history· 23
Single-event upset → Al Holman, Bevatron, Dan Binder, Dr, Edward, Further, Hughes, IBM, IEEE Transactions, In, Inch Cyclotron, James Ziegler, Lanford, Lawrence Berkeley National Laboratory's, May, Nuclear Science, Scientists Dr, SEU, Single-event, Smith
related to Notable SEUs· 13
Single-event upset → Airbus, All, Belgium, Brussels's, In, JetBlue, Maria Vindevoghel, On October, Qantas Flight, Schaerbeek, SEU, The, This
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 105 structured relationships around Single-event upset. Examples in this analysis include Single-event upset → related to Further reading → May and Single-event upset → related to Further reading → Woods. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Single-event upset
related to Further reading
May
0.60
section
Single-event upset
related to Further reading
Woods
0.60
section
Single-event upset
related to Further reading
IEEE Trans Electron Devices
0.60
section
Single-event upset
related to Further reading
ED-26
0.60
section
Single-event upset
related to Further reading
Soft-error
0.60
section
Single-event upset
related to Further reading
JEDEC JESD89A
0.60
section
Single-event upset
related to Further reading
Ziegler
0.60
section
Single-event upset
related to Further reading
Lanford
0.60
section
Single-event upset
related to Further reading
Effect
0.60
section
Single-event upset
related to Further reading
Cosmic Rays
0.60
section
Single-event upset
related to Further reading
Computer Memories
0.60
section
Single-event upset
related to Further reading
Science
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Single-event upset bring nearby vocabulary together. In this analysis, examples include First, Seu and Event. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Single-event upset
First
Seu
Event
Single-event
Upset
Effects
Electronic
Cause
Upsets
Also
Particle
Design
single-event upset
Single
First
Seu
Event
Single-event
Upset
Effects
Electronic
Cause
Upsets
Also
Particle
soft error
Soft
Errors
Memory
Caused
Particles
Terrestrial
Device
Rays
Space
Single
Cosmic
Radiation
radiation effects in electronic devices
Radiation
Design
Upsets
Single-event
Seus
Rays
Electronic
Particles
Terrestrial
Testing
Cosmic
Cause
error correcting memory
State
Soft
Memory
Caused
One
Single
Device
Radiation
Upset
Bit
Effects
Errors
single-event transient
First
Seu
Upset
Effects
Electronic
Cause
Upsets
Also
Particle
Design
Latch-up
Space
testing for seu sensitivity
Single-event
Device
Upsets
Also
First
Caused
Change
Circuits
Electronic
State
Test
Testing
seus and circuit design
Electronic
Testing
Upsets
Particle
Terrestrial
Errors
Often
Circuits
Single-event
Cosmic
First
Latch-up
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Single-event upset, one of the stronger structural bridges in this analysis connects Single-event upset with SEUs and circuit design. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Single-event upset
Nodes49
Edges48
Triples105
Avg. degree1.96
Density0.040816
Components1
Source & methodology
TTTA analyzes the structure around Single-event upset to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Applications, Art & Measurement, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
