Neuromorphic Engineering Methods – Morphing Biology on Silicon

Neuromorphic systems design method involves the mapping of models of perfection and sensory processing in biological systems onto analog VLSI systems which emulate the biological functions at the same time resembling their structural architecture.

These systems are mainly designed with CMOS transistors that enable low power consumption, higher chip density and integration, lower cost. These transistors are biased to operate mainly in the sub-threshold region to enable the realizations of high dynamic range of currents which are very important for neural systems design.

Elements of adaptation and learning (a sort of higher level of adaptation in which past experience is used to effectively readjust the response of a system to previously unseen input stimuli) are incorporated into neuromorphic systems since they are expected to emulate the behavior of the biological systems and compensate for imperfections in the physical implementation and changes in the environment where they operate. Imperfections in physical imperfections could come via circuit elements mismatches while noise and random error could result from environment.

These adaptation and learning implementations are done with analog systems that drive the realization of efficient neural systems based on parallel distributed architectures for low-power, real-time and robust operation.

The process of developing a Chip

The neuromorphic engineering could be divided into neuromorphic modeling, reproducing neuro-physiological phenomena to increase the understanding of the nervous systems and neuromorphic computation which uses the neuronal properties to build neuron like computing hardware. Basically, the former provides the knowledge of the biological algorithm while the latter translates the algorithm into electrical circuits. this is an iterative process since the understanding of the biological algorithm is a very complex process. As more knowledge evolves yielding improved algorithm, the electrical circuits are revised and improved.

These circuits then pass through all the stages of developing integrated circuit (or chip), which involves the circuit layout, verification, fabrication in foundry and testing and subsequent deployment. A brief explanation of each of these steps while examining within the domain of general chip design is provided below:

Layout Design: This stage involves the translation of the circuit realized in the previous stage into silicon description through geometrical patterns aided by CAD tools. This translation process follows a process rule that specifies the spacing between transistors, wire, wire contacts and so on. The layout is designed to represent the electrical circuit schematics obtained from the algorithm.

Fabrication: Upon satisfactory verification of the design, the layout is sent to the foundry where it is fabricated. The process of chip fabrication is very complex. It involves many stages of oxidation, etching, photolithography, etc. Typically, the fabrication process translates the layout into silicon or any other semiconductor material that is used.

Testing: The final stage of the chip development is called testing. Electronic equipment like oscilloscopes, probes, and electrical meters are used to measure some parameters of the chip to verify its functionalities based on the chip specifications.

Due to the complex nature of biological algorithms and translations into electrical circuitries, neuromorphic team comprises of diverse group of experts. Typical areas are biology, electronics, physiology, computer science and engineering. These experts work together to understand the biological systems and consequently develop hardware systems that mimic their behaviors and forms. The complementary metal oxide semiconductor (CMOS), the most common technology in integrated circuit, is the most widely used to develop and fabricate the chips. The CMOS technology has the advantages of low power consumption and capability to pack many circuits elements efficiently together.

Conclusions

Modern advancement in biology is enabling better understanding of forms, structure, and behavior of biological systems to develop algorithms implemented in analog integrated circuits. These circuits are developed in parallel-distributed architectures with elements of adaptation and learning using low power and high integration density technologies. As the algorithms mature, these chips, neuromorphs, are expected to become more efficient for critical industrial application like animal parts replacements. Effective interdependence collaborations among the key areas of biology, electrical engineering, physiology and computer science are very fundamental to develop and engineer this emerging area of computational biology.