Hybrid ATPG (Automatic Test Pattern Generation) algorithm

The project \"NN-for-ATPG\" proposes a hybrid approach for Automatic Test Pattern Generation (ATPG) by integrating machine learning with the FAN algorithm. It includes implementations of two approaches, NN-Hyb and NN-All. It features two implementations, NN-Hyb and NN-All, which apply neural network models at selective and all circuit levels, respectively. Project files support the paper by providing the source code, circuit files, and scripts needed to reproduce key results, including comparisons for \"all fault cases,\" \"hard-to-detect faults,\" and runtime comparisons with and without acceleration. Minimal requirements include a C++ compiler and shell script execution capabilities., , , ## File Structure

This repository includes the following directories and files (full file for download under Related works 'Software'):

* `README.md`: instructions to compile and generate results in the paper
* `NN-Hyb`: directory containing source code of our proposed approach
* `NN-All`: directory containing soruce code of alternative approach compared in the paper
* `circuits`: circuit files used in our experiments (also found on Dryad)
* `hard_faults`: hard-to-detect faults per circuit used in results of Table II in the paper
* `script`: scripts to generate results in Tables I, II, III in the paper

## Installation

In the paper we have compared the following 3 approaches:

(1) Fan: base approach for ATPG used for comparison (2) NN-Hyb: our approach based on applying our neural network (NN) model during backtrace at select levels of the circuit during ATPG (3) NN-All: another alternative approach using our NN model, but at all levels of the circuit

To get an executable for (1), p...