Dublin, Aug. 08, 2018 (GLOBE NEWSWIRE) -- The "Heimann Sensor 32 x 32-Array Thermopile LWIR Image Sensor with Silicon Lens Complete Teardown Report" report has been added to ResearchAndMarkets.com's offering.

This report provides a detailed teardown and cost analysis of the thermopile die, the silicon lens, the EEPROM die, and the packaging.

LWIR imaging is increasingly used in myriad applications, from consumer to industrial. Low-cost, large arrays (32 x 32 and more) are specifically adapted to smart home/smart building applications for occupant detection, popu-lation localization, population counting, fire detection, and more. For these large markets of many hundreds of millions units a year, thermopile sensors are cost-competitive compared to micro-bolometers.

Based on a low-definition, 32 x 32 thermopile sensor, Heimann Sensor's HTPA32x32d is dedicated to these markets. Cheaper than a microbolometer and easier to integrate, the thermopile offers very good performance for applications that do not require high-resolution images and a high frame rate.

The thermopile array sensor consists only of a 0.5cm camera (with lens). The system is made easy for integrators with a digital IC interface, and includes for the first time a silicon lens for low-cost applications. The 32 x 32 array sensor uses a 90m pixel based on a thermopile technology for a very compact design.

This report also includes a comparison between the characteristics of the new and previous versions of the thermopile sensors from Heimann Sensor, and a comparison with FLIR's ISC1403 microbolometer. This latter comparison highlights differences in each company's technical choices.

Key Topics Covered:

1. Overview / Introduction

2. Company Profile

3. Physical Analysis

4. Comparison - Heimann Sensor HTPA32x32d vs. Flir ISC1403L

5. Manufacturing Process Flow

6. Cost Analysis

7. Estimated Price Analysis

For more information about this report visit https://www.researchandmarkets.com/research/tq7j8j/2018_complete?w=12

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Related Topics: Optical Sensors