Situated in a mountain village of Japan is FANUC’s widely reported lights out factory. This one of a kind, unmanned factory works autonomously 24/7 and is well known for robots that can assemble, test, and monitor themselves. A few decades ago, such a scenario would have existed only in the pages of Isaac Asimov’s science fiction. Today, the FANUC use case is a proof of the dawn of cognitive factories and how far artificial intelligence (AI) has been able to penetrate into the walls of these factories.
More than four decades ago, the programmable logic controller (PLC) automated many of the manual tasks in factories. The question is, can we automate further? Siemens’ vision of the Future of Automation recently showcased this precisely.
Defined as the science behind developing intelligent machines, AI is an advanced form of computer technology that empowers machines to perform tasks that are normally workable only by humans. Also known as cognitive intelligence, AI is the umbrella term used for several underlying technologies like machine learning, computer vision, speech recognition, robotics, natural language processing, deep learning, and so forth.
Cognitive intelligence is not just giving manufacturers the ability to gain answers to known questions; it is also empowering the industry to find new answers to emerging questions. Similar to how earlier revolutions in manufacturing have seen several benefits from lean manufacturing, automation and IT, AI looks very promising as the next lynchpin for Industry 4.0.
AI will become increasingly important over the next decade
As a result of the growing number of machines being connected to the internet, manufacturers are being hit with a huge tsunami of data. Several advancements in data processing and predictive analytics have helped utilize this data to generate insights for effective decision-making. The voluminous nature of manufacturing data further facilitates cognitive technologies to produce artificially intelligent systems that can correlate information, recognize patterns, and identify solutions or opportunities in manufacturing. Currently, most machines are embedded with low-level logical processors that demand a considerable amount of human intervention for making logical decisions. Cognitive manufacturing is the next evolutionary step, in which machines would autonomously begin to detect changes in the manufacturing process and would know how to respond real-time to the constantly changing manufacturing scenario with minimal human intervention.
Big Data, Data Analytics, and Internet of Things together have resulted in data explosion. The process of Big Data encompasses data collection from end points through field nodes such as sensors and micro-electro-mechanical systems (MEMS) and transmitting them over network chips and network infrastructures to be processed in datacenters. The challenge in handling and processing such data stems not only from its huge volume, but also from the unorganized structure, which calls for high computing power to segregate the right data from noise, organize it, and produce meaningful business insights–all in a short time frame. Traditional computing architectures, such as the von Neumann architecture, pose challenges for continued adoption. Therefore, computing chips are witnessing new developmental approaches, including changes to architecture, materials, and a focus on utilizing accelerators, ASICs. However, in the short term, it is expected that the current adoption of field programmable gate array (FPGA) and GPU including general purpose GPU (GPGPU) will continue to be the preferred alternative.
Memory chips are the key devices to store and manage data in datacenters. Their performance being crucial to the success of datacenter operations, there is a strong demand to improve the performance of the current double data rate (DDR) class of memories. Memory devices are not only expected to handle huge volumes of data with high bandwidth, but high performance has to be demonstrated with optimized cost and low power consumption. It is expected that the nextgeneration memory devices such as high bandwidth memory (HBM) and graphics DDR (GDDR) will be adopted in datacenters. However, in the near future, DDR4 buffer chips and DDR5 are expected to be utilized in datacenters.
While the industry is looking to advance memory classes for datacenters, there is an alternative approach to push computing and memory handling to the edge network. However, this needs an improved memory class. NVDIMM-P, a hybrid dual in-line memory module (DIMM)-based memory technology, is among the expected storage classes of memory that can bring such benefits. The NVDIMM is a persistent non-volatile class of memory device that also enables bringing the processor closer physically, allowing for faster processing of large data volumes. Therefore, the industry is hoping to utilize NVDIMMs in IoT devices to avoid or divert huge influx of traffic in the network.
The semiconductor industry is striving to continue with Moore’s law to achieve success in applications such as datacenters. However, the industry is finding it economically and scientifically hard to scale beyond 10 nanometer (nm) structures. As the industry tries to progress, advances are also being explored in packaging applications, including interconnects to deliver the benefits required in end-user applications such as datacenters. Interconnect technologies are being widely used in datacenter applications to lay down two or more different chips on a single interconnect substrate, such as the silicon interposer, that can establish an electrical connection between the two. Simply put, interconnects enable heterogeneous chip connections for 2.5D and 3D integrated circuits (ICs) before being placed on the printed circuit boards (PCBs), significantly improving the processing speed.
Research efforts are also being focused on interposer applications to explore new materials such as the glass interposer, which could deliver high input/output (i/o) connection density at a much lower cost.
In a nutshell, datacenter growth is enabling a wide range of development activities across different semiconductor devices and packaging technologies. With the demand for data increasing rapidly, semiconductors will continue to advance and grow to achieve new landmarks.