SFM3000 is a cost effective fingerprint identification module equipped with essential part for fingerprint identification and template storage. SFM3000 is suitable for most of the applications where it processes fingerprint recognition and host controller is used to handle other operations. The communication between SFM3000 and host controller is done by packet protocol through CMOS level serial interface.
Features
High Performance 400MHz DSP
Fast Power-on Time
Suprema, ISO19794-2 and ANSI 378 Template Options
8 Configurable Digital I/O Ports
Low Power Consumption
256-bit AES Fingerprint Data Encryption
Available Models
SFM3050-TC
UPEK TouchChip Sensor
Capacitive type sensor
PerfectPrint technology
Reliable high quality fingerprint image
SFM3050-TC2S
UPEK Steel-Coated TouchChip Sensor
IP67 rated dust and water protection
Capacitive sensor
PerfectPrint technology
Reliable high quality fingerprint image
Specification
Sensor Option
Capacitive
CPU
400MHz DSP
Flash Memory
1 MB (4MB Option)
EER
< 0.1%
Enrollment Time (Sensor Dependent)
< 800ms TC1 < 500ms TC2/TC2S
1:1 Verification Time (Sensor Dependent)
< 800ms TC1 < 500ms TC2/TC2S
1:1000 ID Time* (Sensor Dependent)
< 970ms TC1 < 640ms TC2/TC2S
Template Options
Suprema, ISO19794-2, ANSI-378
Template Size (Default: 384 Bytes)
Default: 384 Bytes 256 - 384 Bytes (Configurable)
Template Capacity
1,900 @ 1MB Flash 9,500 @ 4MB Flash
Host Comm.
Asynchronous serial: CMOS level (3.3V) (up to 460800 bps)
External I/O
8x Digital I/O
Fingerprint Data Encryption
256-bit AES
Supply Voltage
3.3 VDC Regulated
Operating Temp (°C)
-15 to 50 °C
Board Size (L x W x H) (mm)
55 x 40 x 8
Model
SFM3050-TC1
SFM3050-TC2
SFM3050-TC2S
Sensor Type
Capacitive
Capacitive
Capacitive
Sensor Options
TC1
TC2
TC2S
Resolution (dpi)
508
508
508
Sensing Area (mm)
12.8 x 18.0
10.4 x 14.4
10.4 x 14.4
Image Size (px)
256 x 360
208 x 288
208 x 288
Finger Rotation
+/- 90'
+/- 90'
+/- 90'
Sensor Dimension (L x W x H) (mm)
20.4 x 27 x 3.5
20.4 x 27 x 3.5
20.4 x 33.4 x 3.57
*Average genuine identification time including feature extraction