Automatic test equipment or automated test equipment (ATE)

Lecture 3а Automatic Test Equipment

Author of presentation

Ivanova Elvira

Electronic test equipment

• E TE  is used to create signals and capture responses from electronic  Devices Under Test  (DUTs). In this way, the proper operation of the DUT can be proven or faults in the device can be traced. Use of electronic test equipment is essential to any serious work on electronics systems.

Electronic test equipment

• Practical  electronics  engineering and assembly requires the use of many different kinds of electronic test equipment ranging from the very simple and inexpensive (such as a  test light  consisting of just a light bulb and a test lead) to extremely complex and sophisticated such as  Automatic Test Equipment . ATE often includes many of these instruments in real and simulated forms.

Types of test equipment

• The following items are used for basic measurement of voltages, currents, and components in the circuit under test.
• Voltmeter  (Measures  voltage )
• Ohmmeter  (Measures  resistance )
• Ammeter , e.g.  Galvanometer  or Milliameter (Measures  current )
• Multimeter  e.g., VOM (Volt-Ohm-Milliameter) or DMM (Digital Multimeter) (Measures all of the above)

Types of test equipment

• The following are used for stimulus of the circuit under test:
• Power supplies
• Signal generator
• Digital pattern generator
• Pulse generator
• Howard piA digital multimeter
• The following analyze the response of the circuit under test:
• Oscilloscope (Displays voltage as it changes over time)
• Frequency counter (Measures frequency)
• And connecting it all together:
• Test probes

Vistec Fully Automated Infrared Inspection And Review System

• The brandnew IRIS2000 will be used to control glued wafers in the customer's production. Inspection with infrared light enables to see "through" Silicon wafers. Therefore, infrared inspection can detect defects which are not visible by any other illumination method.

Automatic test pattern generation

• A wide range of measuring possibilities from CD and Overlay, defect sizes to fill and mould parameters, allows effective monitoring of the whole production process.
• Combining fast imaging with high defect sensitivity, the system offers prompt and reliable analysis and highly reproducible results.

Automatic defect classification for semiconductor manufacturing

• Classification (ADC) automates the tedious manual process of defect review and classification during optical microscopy and scanning electron microscopy (SEM); a key step in the identification of the root cause of a manufacturing problems. ORNL has been working with the semiconductor industry since 1991 to provide new strategies, methods, and technologies for defect detection, segmentation, and classification for the purposes of process characterization, control, and rapid yield improvement.

Automatic defect classification for semiconductor manufacturing

• The Vistec ADC technology provides flexible investigation and development tools for front-end-ofline (FEOL) and back-end-of-line (BEOL) inspection and review environments and is used for both optical and SEM imaging modalities. The technology currently is in commercial use for SEM defect review and post-fabrication optical inspection systems

Automatic test pattern generation

• Algorithmic methods
• Testing  very-large-scale integrated  circuits with a high  fault coverage  is a difficult task because of complexity. Therefore many different ATPG methods have been developed to address  combinational  and  sequential  circuits.

Automatic test pattern generation

• Early test generation algorithms such as  boolean difference  and  literal proposition  were not practical to implement on a computer.
• The  D Algorithm.   D Notation  
• Path-Oriented Decision Making  (PODEM)
• Fan-Out Oriented  ( FAN Algorithm )
• Pseudorandom test generation
• Wavelet Automatic Spectral Pattern Generator  (WASP)

Base Technology

• The Vistec ADC methods use extensive monochrome and multispectral features; principle component and linear discriminant feature analysis; and pair-wise, multiclass, and fuzzy implementations of k-nearest neighbor and radial basis function classifiers.

Automatic defect classification for semiconductor manufacturing

• A unique classifier training mechanism and interface provides intuitive visual and numeric feedback to assist in maintenance of defect libraries.