Optical Sensors - Photo Detectors

Optical Sensors - Photo Detectors - Logic Output

Image	Part Number	Description / PDF	Quantity	Rfq
	IS489E Sharp Microelectronics	LIGHT DETECTOR OPIC 900NM	0
	IS489 Sharp Microelectronics	LIGHT DETECTOR OPIC 900NM	0
	IS471FSE Sharp Microelectronics	LIGHT DETECTOR OPIC 940NM STRGHT	0
	IS471F Sharp Microelectronics	LIGHT DETECTOR OPIC 940NM	0

Optical Sensors - Photo Detectors - Logic Output

1. Overview

Photo detectors with logic output are optoelectronic devices that convert light intensity into digital output signals (typically TTL or CMOS levels). Unlike analog photodetectors, these devices integrate signal processing circuits to provide binary "on/off" outputs based on predefined light thresholds. They play a critical role in automation systems, consumer electronics, and industrial equipment by enabling precise optical event detection without external signal conditioning.

2. Main Types and Functional Classification

Type	Functional Features	Application Examples
Photointerrupter	Slot-based design with emitter/detector alignment verification	Encoder position detection, paper presence detection in printers
Reflective Sensor	detects reflected light from target objects	Color recognition, proximity detection in smartphones
Logic Output Photodiode Array	Multi-element sensing with built-in comparators	Edge detection in industrial vision systems
Ambient Light Cutoff Sensor	Rejects ambient light interference	Display brightness control in automotive HMIs

3. Structure and Components

Typical construction includes:

Photodiode or phototransistor light sensing element
Integrated transimpedance amplifier (TIA)
Voltage comparator with reference threshold
Digital output driver circuitry (push-pull or open-collector)
Optical filter (wavelength-specific variants)

The components are housed in standard packages like TO-92, SMD, or custom optomechanical enclosures with defined optical apertures.

4. Key Technical Specifications

Parameter	Importance
Response Time (ns)	Determines maximum operating frequency
Dark Current (nA)	Affects low-light detection accuracy
Output Type (NPN/PNP)	Dictates interface compatibility
Operating Wavelength (nm)	Matches light source spectrum
Supply Current (mA)	Impacts power consumption budget
Environmental Temperature Range	Defines operational reliability limits

5. Application Fields

Major industries utilizing these sensors include:

Industrial Automation: Conveyor belt object counting, robotics gripper feedback
Consumer Electronics: Touchless switch interfaces, camera autofocus assist
Medical Devices: Blood oxygen saturation monitoring, fluid level detection
Automotive: Rain sensor for wipers, cabin occupancy detection
Smart Home: Appliance status monitoring, security system intrusion detection

Example: In automatic faucets, reflective sensors detect hand presence within 50ms response time while rejecting ambient light interference.

6. Leading Manufacturers and Products

Manufacturer	Representative Product	Key Features
Vishay Semiconductor	VEMD8081	15ns response time, 55mA supply current, automotive qualified
Sharp Microelectronics	GP1US53SCSF	Integrated IR emitter, 10kHz modulation frequency
STMicroelectronics	STALS3000A	Dual-channel output, programmable threshold, I2C interface
ON Semiconductor	BPW34FS	High radiant sensitivity (0.85A/W), moisture-resistant package

7. Selection Guidelines

Key considerations:

Determine detection mode: transmissive vs. reflective vs. ambient rejection
Match wavelength sensitivity to light source (850nm IR vs. visible spectrum)
Evaluate environmental factors (temperature, humidity, EMI exposure)
Verify output compatibility with host system voltage levels
Assess mechanical requirements (package size, mounting orientation)
Consider built-in features like hysteresis or temperature compensation

For high-vibration environments, select sensors with mechanical locking features and shock ratings above 15G.

Industry Trends

Emerging developments include:

Miniaturization: Wafer-level packaging enabling 1.0x0.6mm form factors
AI Integration: On-chip machine learning for pattern recognition
Energy Harvesting: Self-powered sensors using photovoltaic principles
Spectral Tuning: Adjustable wavelength selectivity via MEMS filters
Wireless Interface: Bluetooth 5.0 enabled sensors for IIoT applications

Market forecasts predict 8.7% CAGR through 2027, driven by automotive LiDAR and smart home automation demand.