Title: Identifying defects during semiconductor manufacturing using Machine Learning

IoT is networking of physical devices and computers which enables them to collect and share data. The collected data is usually aggregated and stored on cloud platforms. IoT allows remote sensing and monitoring of these devices. This internetworking and connectivity is allowing automation in various fields. One such great example is that of industrial IoT (IIoT). The IoT enabled manufacturing systems enable monitoring of vital machine data and controlling the machine using various signals. This helps to improve the manufacturing process and helps to plan maintenance activities of the machines.

DATA SET INFO

Data from a semi-conductor manufacturing process.

Data Set Characteristics: Multivariate

Number of Instances: 1567

Area: Computer

Attribute Characteristics: Real

Number of Attributes: 10

Associated Tasks: Classification, Causal-Discovery

Missing Values? Yes

Variable	Description
Room_temp	Room Temperature
Current_leakage	Taken as a Amp or mA.
Voltage_leakage	Taken as a volt or mV.
cmos_design	measuerment of dimension
Cut_off_freq	The final limit taken
Silica_effect	Taken during wafer manufacturing
Avoid_dust_particle	denisty of air and dust
Avoid_uv_light	the amount of darkness and light
Wafer_process	overall design

Output screen is Minimalistic yet Effective.