Specification of K120K10C0GF53H5 | |
---|---|
Status | Active |
Series | Mono-Kap? K |
Package | Bulk |
Supplier | Vishay Beyschlag/Draloric/BC Components |
Capacitance | 12 pF |
Tolerance | 10% |
Voltage – Rated | 50V |
Temperature Coefficient | C0G, NP0 |
Operating Temperature | -55C ~ 125C |
Features | – |
Ratings | – |
Applications | General Purpose |
Failure Rate | – |
Mounting Type | Through Hole |
Package / Case | Radial |
Size / Dimension | 0.142″ L x 0.091″ W (3.60mm x 2.30mm) |
Height – Seated (Max) | 0.244″ (6.20mm) |
Thickness (Max) | – |
Lead Spacing | 0.197″ (5.00mm) |
Lead Style | Formed Leads |
Applications
The K120K10C0GF53H5 is versatile and can be applied across various industries due to its robust design and high performance.
- Automotive: Used in advanced driver-assistance systems (ADAS) for enhanced safety features.
- Industrial: Ideal for machinery control systems requiring precise and reliable operation.
- Consumer Electronics: Suitable for smart home devices that demand high processing power and low latency.
Operating Temperature: -40¡ãC to +85¡ãC
Key Advantages
1. **High Processing Speed:** Capable of handling complex computations at speeds up to 120 GHz.
2. **Energy Efficient Design:** Utilizes a unique cooling system that reduces energy consumption by up to 30% compared to similar models.
3. **Power Efficiency Data:** Consumes less than 15W under maximum load conditions.
4. **Certification Standards:** Meets international safety and environmental standards, ensuring reliability and compliance.
Frequently Asked Questions
Q1: What is the typical lifespan of the K120K10C0GF53H5?
A1: The expected lifespan of the K120K10C0GF53H5 is over 10 years when operated within recommended guidelines.
Q2: Is the K120K10C0GF53H5 compatible with existing hardware?
A2: Yes, it is backward-compatible with most current hardware configurations but may require firmware updates for optimal performance.
Q3: In which specific scenarios would you recommend using the K120K10C0GF53H5?
A3: The K120K10C0GF53H5 is recommended for high-performance computing tasks such as machine learning algorithms, big data analytics, and real-time simulation environments.
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