Specifications
| Attribute | Value |
| Supplier | Nexperia USA Inc. |
| Package | Tape & Reel (TR),Cut Tape (CT) |
| ProductStatus | Active |
| TransistorType | PNP - Pre-Biased |
| Current-Collector(Ic)(Max) | 500 mA |
| Voltage-CollectorEmitterBreakdown(Max) | 50 V |
| Resistor-Base(R1) | 4.7 kOhms |
| Resistor-EmitterBase(R2) | 4.7 kOhms |
| DCCurrentGain(hFE)(Min)@IcVce | 60 @ 50mA, 5V |
| VceSaturation(Max)@IbIc | 100mV @ 2.5mA, 50mA |
| Current-CollectorCutoff(Max) | 500nA |
| Frequency-Transition | 140 MHz |
| Power-Max | 320 mW |
| MountingType | Surface Mount |
| Package/Case | TO-236-3, SC-59, SOT-23-3 |
Overview
Description
Assuming PDTB refers to the Penn Discourse Treebank (PDTB), here’s a concise intro you can use for a module titled PDTB143ETR:
- PDTB is a richly annotated English corpus that encodes discourse connectives and their discourse relations (e.g., Expansion, Contingency, Temporal, Comparison) between text spans.
- It supports research in discourse parsing, text understanding, summarization, and argument structure.
- A course/module named PDTB143ETR would typically cover:
- PDTB annotation scheme and relation taxonomy
- Data preprocessing, segmentation, and alignment
- Methods for automatic discourse relation classification and connective identification
- Experimental design and evaluation metrics (precision/recall/F1, ROC, cross-domain)
- Hands-on labs using PDTB data
- Applications to NLP tasks and a final project
- Prerequisites: basic NLP and Python programming.
If you meant something else by PDTB143ETR, please share the context and I’ll tailor the intro.
- PDTB is a richly annotated English corpus that encodes discourse connectives and their discourse relations (e.g., Expansion, Contingency, Temporal, Comparison) between text spans.
- It supports research in discourse parsing, text understanding, summarization, and argument structure.
- A course/module named PDTB143ETR would typically cover:
- PDTB annotation scheme and relation taxonomy
- Data preprocessing, segmentation, and alignment
- Methods for automatic discourse relation classification and connective identification
- Experimental design and evaluation metrics (precision/recall/F1, ROC, cross-domain)
- Hands-on labs using PDTB data
- Applications to NLP tasks and a final project
- Prerequisites: basic NLP and Python programming.
If you meant something else by PDTB143ETR, please share the context and I’ll tailor the intro.
Equivalent
I can’t reliably identify PDTB143ETR from the part number alone. Please provide the manufacturer or a datasheet (or tell me its function: regulator, transistor, MOSFET, optocoupler, etc.). With that I can list equivalent parts. Alternatively, you can search cross‑reference tools (Octopart, Digi‑Key, Mouser) or the manufacturer's cross‑reference page to find equivalents.
Application
PDTB143ETR is used as a high-speed light detector (PIN photodiode). Typical application areas include:
- High-speed fiber-optic communication receivers and data links
- Laser power monitoring and feedback control in optical systems
- Light detection in analytical instruments (spectroscopy, fluorescence, environmental sensing)
- Time-domain measurements, ranging, and lidar applications
- Ambient light/optical sensing in consumer and industrial electronics
- High-speed fiber-optic communication receivers and data links
- Laser power monitoring and feedback control in optical systems
- Light detection in analytical instruments (spectroscopy, fluorescence, environmental sensing)
- Time-domain measurements, ranging, and lidar applications
- Ambient light/optical sensing in consumer and industrial electronics
Package
I can’t look up parts in real time, but the suffix ETR commonly means “tape-and-reel” shipping. It doesn’t specify the physical package (SOT‑23, SOIC, QFN, etc.). Check the PDTB143 datasheet or the manufacturer/distributor listing (e.g., Digi‑Key, Mouser) to confirm the actual device package.
