PGA Sockets Pin Grid Array Socket
Antenk's processor socket line is designed for use with Intel- and AMD-based microprocessor packages. Socket types include land grid array (LGA), micro pin grid array (mPGA), and PGA with low to zero insertion force. The mPGA and PGA sockets are designed for various microprocessor packages for notebook PCs, desktop PCs, and servers. For custom applications, the compression sockets can be configured to the specific application.
Pin Grid Array (PGA) Sockets Type
PGA Sockets,mPGA Sockets,Integrated Circuits Socket,Pin Pga Sockets,Pga Socket Connector,PGA Adapters,Pin Grid Array Sockets,Processor Socket ShenZhen Antenk Electronics Co,Ltd , https://www.antenk.com
A pin grid array, often abbreviated PGA, is a type of integrated circuit packaging. In a PGA, the package is square or rectangular, and the pins are arranged in a regular array on the underside of the package. The pins are commonly spaced 2.54 mm (0.1") apart, and may or may not cover the entire underside of the package.
PGAs are often mounted on printed circuit boards using the through hole method or inserted into a socket. PGAs allow for more pins per integrated circuit than older packages, such as dual in-line package (DIP).
PGA Sockets & Adapters
Low insertion force Pin Grid Array (PGA) Sockets and Adapters are available in a variety of RoHS Compliant insulators with hundreds of screw-machined terminal choices. Virtually any PGA footprint can be accommodated, including interstitial patterns.
PGA Sockets & Adapters Overview
Durable construction for virtually any application
Wide variety of materials, lengths, and sizes
Cost-effective method for replacing, repairing, or upgrading PGA devices
Unique options such as solder preform terminals eliminate the need for wave soldering in mixed SMT/Thru-hole applications
RoHS compliant insulators and screw-machined terminals are compatible with lead-free processing - select either Matte Tin/Gold (MG) or Gold/Gold (GG) plating
Antenk's Pin Grid Array (PGA) Sockets
Complex printed circuits are too valuable to risk direct soldering to expensive integrated circuits (ICs). Using a socket is the answer. The use of sockets offers advantages that prove cost effective and simplify board design.
These sockets provide a zero insertion force (ZIF) PGA interface to the microprocessor PGA package and are attached to the PCB with surface-mount technology (SMT) soldering. PGA sockets are available in arrays up to 989 positions with single lever, screw driver, and hex wrench actuation methods.
PGA Sockets (LIF)
These sockets are primarily employed for microprocessor package test applications using through-hole solder attachment to the PCB design. The contacts are screw-machine outer sleeves with either stamped and formed or drawn inner contacts. Custom arrays are available in more than 1,000 positions.
mPGA
PGA
Eliminate hand-loading of pins, facilitate solder joint visibility, low profile component mounting or board mating.
Principle of Neural Network Algorithm_Application of Neural Network Algorithm_An Example of Neural Network Algorithm
Neural networks are computational models inspired by the structure and function of the human brain. They operate on the principle of distributed information storage and parallel processing, allowing them to handle complex tasks efficiently. While each individual unit in a neural network performs a simple operation, when combined in large numbers, these units can process intricate data patterns and form highly sophisticated nonlinear dynamic systems capable of learning from data.
The architecture of neural networks closely resembles the human brain, featuring massive parallelism, distributed storage, self-organization, self-adaptation, and self-learning abilities. These features make them widely applicable across various domains, including system identification, pattern recognition, and intelligent control. In recent years, tech giants have shown particular interest in their automatic learning capabilities, especially in areas where uncertainty and ambiguity are common, such as voice recognition and natural language processing.
**Principle of Neural Network Algorithm**
Although the design of neural network algorithms is quite extensive, we will focus on the Microsoft neural network algorithm for now. The model typically consists of three layers: the input layer, an optional hidden layer, and the output layer. This type of network is known as a multilayer perceptron.
The **input layer** contains neurons that represent the attributes and probabilities of the input data. These values serve as the starting point for the network's processing.
The **hidden layer**, if present, acts as an intermediary between the input and output layers. Hidden neurons receive inputs from the previous layer, apply weights to them, and pass the results to the next layer. These weights determine the importance of each input, with higher weights indicating greater influence. This weighting process is central to the learning mechanism of the network.
The **output layer** produces the final predictions or classifications based on the processed information. Data flows through the network in a forward direction, with each layer passing weighted and transformed data to the next. This is known as the forward propagation process.
When the output deviates from the expected result, the network adjusts its weights through a process called backpropagation. This feedback mechanism allows the network to "learn" from its mistakes by reducing the impact of incorrect inputs and reinforcing correct ones. This iterative process continues until the network achieves satisfactory accuracy.
Each neuron in the hidden layers applies a nonlinear activation function, which enables the network to model complex relationships. These functions mimic the behavior of biological neurons, producing significant output changes even from small input variations.
**Application of Neural Network Algorithm**
Artificial neural networks have been applied in numerous practical systems, such as signal processing, pattern recognition, expert systems, robotics, and the control of complex systems. Their ability to learn and adapt makes them powerful tools in many scientific and industrial fields.
Throughout the development of science and technology, humans have faced challenges in exploring space, understanding particles, and uncovering the origins of life. Similarly, research into brain function and neural networks will continue to evolve as new obstacles are overcome.
While neural networks are currently widely used in speech recognition, their applications extend far beyond that. One promising area is image processing, where they can identify objects by analyzing visual features layer by layer. For example, in image recognition, the first layer detects edges, the next identifies corners, and subsequent layers combine these features to recognize complete objects.
Google’s research team has already developed software capable of identifying cats in online videos using this approach. Future applications could include image search engines and tools like Google Street View, which could use neural networks to detect and classify objects in real-world images.
In the medical field, neural networks also show great potential. Researchers at the University of Toronto have successfully used them to analyze how drug molecules interact with biological systems, opening new possibilities for drug discovery and treatment development. As the technology advances, the applications of neural networks will only continue to expand.