mpw-005 / INDM Inductor-TLM

the INDM shapes of all of mpw-005

40 of 40 dies rendered.

11,495,776 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (36.3 MB in total, largest 3.1 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-005_INDM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-005_INDM_reticle.png
the stitched reticle, full resolution
21.8 MB b72ce769c21f3f561622aa8d9253b765822f0f542030eef0c6496a0c0c8ad6918646192d 36ffca38d97ae3225d14cd4c0a302d3794b172d613aa3ea7a753895027e6382d
mpw-005_INDM_reticle_overview.png
the same image scaled to fit 4096 px
2.0 MB bdeebd3b52291b5665d6331e6d2ee9298c0bc4812f1a5b84d52d039d1d4d87509960835e 7e59083c17cccf455527b882ebf0189b678ab2586cbe8b1b7a7a841111e8e974
mpw-005_INDM_tiles.json
where every tile belongs, and its checksum
13.5 kB 44557af6197f6ee8417aa2052232ce55b8cdeb3b8096a4295fe90d51e3a70b41f5071cc0 d019597d3d6de698769687798bab78865714fc91e9ba7301bee3fea9df588c97
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 641.7 kB1ce0e92cdbd2bf98b28af69bac8c19373e7d2c3e31510bc469a0baf40de03633
A2 mpw5_cache 7576×107767576, 0 701.5 kB334b3b413367eaf6b2dd7e86f6a361ad43a9d839c82f7b3601067e20d1577233
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 1.1 MB7b5e51b87989785d1eb88a43edfbf3c8d0621bfb5689e9a345c566cbdfb937ed
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 939.0 kB9f55e03cca7c16fd4723d656c6615bfb500250bd905260a227541865a09a529d
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 861.4 kBce4f30c4a9b65d59e614b314866afe7a0688de5b460f8e03550c5382bb4f5acd
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 1.2 MB016614a40f49b689524e22c51a260bdc3189e32ad12bbc09545b2b2df4dda36c
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 687.2 kBa2686ca4c03e6fec0755d29275c4063cd3e59bab2cc37a58e4b58a654e3d9133
B2 microwatt_mpw5 7576×107767576, 10776 2.0 MB6d1f523eae10e49184151f15f90deff3c7ca975a45cc680b8f1324648a00d029
B3 armleo_gpio_mpw5 7576×1077615152, 10776 758.9 kB3ace2755bd471340904b53194065035268d7dfc3a7a4ea7b34c5b0a3ada9a443
B4 fabulous_efpga 7576×1077622728, 10776 3.1 MBca81371bbe4657258afb860457cd4566e232b862bb881420d6640bdda99e7080
B5 i2c_controller 7576×1077630304, 10776 678.1 kB8ee8d9d9b7801bd31270656973e263407e324c136e9e69a0952f6d663cc8d502
B6 lbist-mbist 7576×1077637880, 10776 1.1 MBb082f0e1c04f9967dd61b5d8c967fd2c4ae7f30567d844a507138f8d7a6e948d
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 1.2 MB5d8c8d692cfbbdfea0e71087e04d4267431bc55cb77e880f87eedaa8a66ac6dc
C1 riscduino-qcore__q0_ 7576×107760, 21552 1.2 MBcda48d0abffcd285171f3e4d17b98b79b017f15dbbfd1e2125c779c94ad69a67
C2 high_speed_vco 7576×107767576, 21552 642.0 kB416c64f6c578c1dce39205cc6ad637a6103db5878a73de12d1fa8658747bf8ff
C3 openfasoc-cryo-gen 7576×1077615152, 21552 687.0 kBb67213d56f6f3ca4928d02aa6782d76f3cd55f410d914d4442a2025819d00262
C4 microwave_signal_generator 7576×1077622728, 21552 751.7 kB1cdb2d3b85fff1853f89e6189521eff61660b81fcc4616eba561789fab45ea1f
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 722.1 kB1624bed6454a517ddf62011db635c09aea2b96631ca29a51b2608d15d9a31cd3
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 648.4 kB8ec6020a1b9600a997e34ceec75ce5e7d74c51e94650f8a84826ffc82207b98b
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 642.2 kBbe415840abab4523c6bc71fb972a1e62e37cdb8ff9141f2bde2d2f896198967b
D2 azadi_iii 7576×107767576, 32328 1.5 MB14adfe4cffc2313dfb0a88ba98f5f6a092d4b04b154df1326539974ba30a78e5
D3 tia_for_physic_experiments 7576×1077615152, 32328 652.4 kB7f5e3f2fbefce2c8eebc394c79740345cd0309b6901798d6f5753e5c3db6dd00
D4 qf105 7576×1077622728, 32328 985.5 kB534050018193e9b97c79e5d80910dd244285f42e209fee091a50f10764173118
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 735.5 kBf8fc7196ef931aed64d48e7304683209460af8bfc3570f6ca62590dd20594f87
D6 junga_soc_mpw5 7576×1077637880, 32328 858.8 kBd6c5e57969d0147d5f0632ec7a460d3596beb76d7d1406e955c2dabdf80c776c
D7 nand_flash_mpw-5 7976×1077645456, 32328 669.6 kB01def214616b8b0d3f5b15724d45596715aa2d1b6bbcefd746cbcbf737c7dc84
E1 kasirga-c0-mpw-5 7576×107760, 43104 835.2 kB05869fc4da6f44c477b620997c1569d1e19ba6ebe82ab5545a0cbf398b4d09cf
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 877.7 kBdd5eae68a0e5a1faf360a8543bfc0caf9f1e4db61306de8280b4d9b7d45dcdc3
E3 wirelessenergyharvesting 7576×1077615152, 43104 645.3 kB14928094e2605beb135bb52065d804a3822afe152847ac081b2d6add0e774e0c
E4 caravel_raster_engine 7576×1077622728, 43104 811.8 kBc9021f14df39d2a1c6693edf65527901ade553294b0d53190b78f95c02f98abd
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 643.5 kBefc577149db8793ef0abe041daaa8326df4b634137bf9ce5e1869b30eac820c3
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 699.5 kB648e6f921273647c022785c8f16a15789e051ff4b973e1c68d0cc24c03a3c5e9
E7 alu 7976×1077645456, 43104 686.1 kB992c7e690a9ba5b4842018c4045e45b22d5673d870781977742bb314dc467c76
F1 4ft4 7576×111760, 53880 753.7 kB55dd5a99ed33d3364716ac4e86524812db361e75f4acc570349feaced6e90e66
F2 rng_chaos 7576×111767576, 53880 679.3 kB6cba368696c89714a1527ceb914ea9aa1975c5586c247e659fe72b1af30bc51e
F3 uetrv-ecore 7576×1117615152, 53880 882.3 kBa20708139a7c646a0ef2b5d03831061f5006a3d7d3eb45c5631b35b8d98d4c3c
F4 systolic_array 7576×1117622728, 53880 685.6 kBdc7dc8bb11887aedc868322a8659f48d1deebffbfcd277073cd7a56ae1cc2b10
F5 reram_test 7576×1117630304, 53880 645.4 kB37b3ef9113988455aa4ece7955890035307f30e8b83bf3f45940937a93f26532
F6 secure_memory 7576×1117637880, 53880 735.8 kB087a6ad2512a1f8488840963b15fa6c8dd21b49f3f9482ff41b27a90aedc92fd
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 690.0 kB398dabca2a28db641030f40bdef49e3caf59165a45d8dcf36595bec57f60eff0

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About INDM on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask INDM

Mask layer
INDM — Inductor-TLM
Drawn layer
70:20
Detail
INDM = met3 (thick-last-metal flow, not SKY130)
Used in SKY130
no

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.